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Utilising Cloud Computing for Image Retrieval Optimization in Mobile Devices

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Literature Review
Student Name & CSU ID
Project Topic Title Utilising cloud computing for the optimization of image retrieval process used in mobile devices
1

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Version 1.0 _ Week 1 (5 Journal Papers from CSU Library)
1
Reference in APA format that will be in
'Reference List'
Shen, M., Cheng, G., Zhu, L., Du, X., & Hu, J. (2018). Content-based multi-source encrypted image retrieval
in clouds with privacy preservation. Future Generation Computer Systems.
Citation that will be in the content Shen et. al., 2018
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/
pii/S0167739X17321969
Level of Journal: Q1 Privacy-preserving, Secure, image retrieval, Multi-source,
Content-based image retrieval, Searchable encryption, Image
encryption
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Homomorphic encryption
Tools:
Multiple Image owners with Privacy
Protection
Applied Area:
Medical diagnoses
Art collection
Problem:
The major problem with the Content-based
image retrieval major issue is that when
images are outsourced to the servers it has
chances of data leakage.
Goal: The goal is to overcome problems and
make data secure and to allow Multiple Image
owners with Privacy Protection (MIPP).
 Image owner
 Keys
 The cloud
 The KMC
The Process (Mechanism) of this Work; The process steps of the Technique/system
2

Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
Generation of key: A secret image encryption
key is obtained so that authorized users and
image owners can use it.
It will ensure image content privacy and its
features privacy.
N/A
2
Encryption and decryption of image: A
standard key is used for image encryption
which is secured by Chosen-Plaintext Attacks
(CPA).
The only authorized user can communicate
with image encryption and decryption key.
N/A
3
Encrypting the features of image: Used for
preserving the image features and features
should be encrypted before uploading it to
cloud.
Without communicating with others the owner
of image and user can select it. Also,
parameters can be discarded.
With KMC, the algorithm is used to decrypt
images.
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Retrieval scheme Images
Input and Output Critical Thinking: Feature of this work,
and Why (Justify)
Critical Thinking: Limitations of the
research current solution, and Why
(Justify)
3

Input (Data) Output (View)
 Input in the medical sector
is a medical image.
Image retrieval is done
efficiently and images are
gathered with a guarantee of
maintaining the privacy
aspects.
The techniques used in this increases the
privacy on an image and make it secure.
Also, accuracy while retrieval increases.
The third point is efficiency. It shows the
time used for retrieving a secured image.
The major limitations are:
 Privacy of images and features of
images that belong to the different
owner their privacy should be
ensured.
 Eavesdroppers
 Cloud
(Describe the research/current solution) Evaluation Criteria How this research/current solution is
valuable for your project
In this, an image retrieval scheme that is used in clouds with
privacy concerns using content-based multi-source encryption
technique is explained. Also with secure multi-party computation,
we used features of the encrypted image so that owner of that
image can encrypt the features by his own key (Shen et. al., 2018).
Image owner provides image database.
Then an authorised user demands an image
retrieval. The cloud is responsible for
storing and retrieving an image. And at last
Key management centre is responsible for
managing encryption and decryption key.
This solution helps in CBIR with
multiple image sources. And it helps in
maintaining the image content privacy
plus the features of the image.
Diagram/Flowchart
4

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Figure: System Model of the Secure Multi-Source CBIR Scheme.
5

2
Reference in APA format that will be in
'Reference List'
Xia, Z., Xiong, N. N., Vasilakos, A. V., & Sun, X. (2017). EPCBIR: An efficient and privacy-preserving
content-based image retrieval scheme in cloud computing. Information Sciences, 387, 195-204.
Citation that will be in the content Xia, et. Al., 2017
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/
pii/S0020025516321971
Level of Journal: Q1 Locality-sensitive hashing, Content-based image retrieval,
Searchable encryption, Secure k-nearest neighbour algorithm
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
k-nearest neighbour
min-hash algorithm
Tools:
Mobile computing
Cloud computing
Applied Area:
Mobile Commerce
Mobile Healthcare
Mobile Gaming
Problem:
The major problem is to store images on cloud
servers. Also, privacy is not preserved for
sensitive images.
Goal:
The two major goals are, first is to maintain
efficiency and second is to maintain the
security of data.
 Keys
 Images
 Cloud
6

Mobile Learning
Mobile social networking
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 The generation of unencrypted index: By
some methods image feature vector is extracted.
It helps to rank query image with the help of
one-one map index.
N/A
2 The index encryption:
The pain-text based information is revealed via
image feature vector format regarding the
content of image
It helps in saving the time. N/A
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Private content-based image retrieval Privacy of unencrypted image.
Homomorphic-encryption High complexities
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
Data: text and images These image features
These encryption techniques and algorithm help to
maintain the privacy of an image. Also in index
feature vectors in trapdoors are encrypted and are
The security of image features should be
improved. And the extraction features of
encrypted image help the future work for securing
7

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The data in these two
format are considered
to be the input for this
module.
are secured against
ciphertext and
efficiency is also
improved.
secured with the k-NN algorithm. These trapdoors
protect information from leakage and maintain
privacy.
CBIR outsourcing.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
In this, a proposal is given that supports CBIR,
and the encoded imaginings are revealed and
information which is sensitive, is forwarded to
cloud. Also, feature vectors are used for
representing the images. With locality sensitive
hashing searching efficiency increases. And with
a k-nearest neighbour (kNN) algorithm features
are secured (Xia, et. Al., 2017).
There is one image owner and one user. They need
encryption and decryption technique to be used.
The security of information is an important factor.
And also the data should be efficient enough.
kNN algorithm helps in protecting feature vectors,
as these help in enabling cloud server for ranking
the search results without any additional
communication burdens in an efficient way. With
local sensitive hashing, pre-filter tables are
created. Based on Euclidian distance methods are
purpose that can be compliantly comprehensive to
the CBIR.
Diagram/Flowchart
8

Figure: A framework with CBIR scheme for preserving privacy.
9

3
Reference in APA format that will be in
'Reference List'
Yang, J., Jiang, B., & Song, H. (2018). A distributed image-retrieval method in the multi-camera system of the
smart city based on cloud computing. Future Generation Computer Systems, 81, 244-251.
Citation that will be in the content Yang. et. al., 2018
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/pii/
S0167739X17321362#sec3
Level of Journal: Q1 Cloud computing, Smart city, Multi-camera system,
Distributed image-retrieval method
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Distributed fault-tolerant processing (DFP)
method.
Tools:
Multi camera system
Applied Area:
Development of smart cities
Problem: The major problem is performing
video sensing for the whole city. Also to
correctly retrieve image in the multi-camera
system is difficult.
Goal:
The goal is to use cloud computing and
improve processing rate.
 Cameras
 Monitor
 Coaxial cable
 Converter
 Connector
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Client Requests: For a particular purpose a It will improve the processing rate. N/A
10

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request is send to cloud computation source.
2 Cloud computation: In this, it considers the
chore and makes a decision.
Fast image retrieval is done. N/A
3 Reply to the client: Final calculated information
and analysis are sent back.
Client get back the helpful information N/A
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Processing rate Cloud computing
Multi-camera system Regenerative code
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
Large information set
is in need to be feed
as input in this journal
as illuminated by the
author.
These are sent to
clients and then the
computation is
performed. And then
again it is sent back to
the client.
In this, it is grounded on a distributed fault-tolerant
processing (DFP) method. The DFP improves
dispensation rate within cloud computing, hence
will overcome slow speed issue of processing rate.
Also dispersed image-retrieval method is
envisioned using cloud-based on a multi-camera
system by using data encryption, data retrieval and
cloud storage.
Dispensation rate based on the cloud has a very
slow speed. In this it mainly used fault tolerant
processing.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
In research distributed fault-tolerant processing
(DFP) method is used. It will improve the rate of
processing. Also, management of image in big
The client is the end user. They need to retrieve the
data from large blocks and in less processing time.
The distributed fault-tolerant processing (DFP)
method will improve the processing rate of cloud
computing. In smart cities, image management is a
11

cities is difficult. But with processing image on
cloud fast image retrieval issue is solved. In this
fault tolerance methods are adopted (Yang. et. al.,
2018).
difficult task. But that has also been solved here.
Image retrieval has been done fastly by using
cloud computing.
Diagram/Flowchart
Figure: a Multi-camera system of smart city
12

Figure: Processing flow ok
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4
Reference in APA format that will be in
'Reference List'
Meng, F., Shan, D., Shi, R., Song, Y., Guo, B., & Cai, W. (2018). Merged region-based image
retrieval. Journal of Visual Communication and Image Representation.
Citation that will be in the content Meng. et. al., 2018
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/
pii/S1047320318301627
Level of journal: Q1 Integrated category matching (ICM), Image retrieval (IR),
Convolution neural networks (CNN), Regional convolution
mapping feature (RCMF)
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Content-based image retrieval (CBIR)
Text-based image retrieval ( TBIR)
Region-based image retrieval (RBIR)
Tools:
AP algorithm
SRM algorithm
Applied Area:
Neural network
Problem: To find images from big data using
textual annotations. The TBIR system is
limited only.
Goal: The goal is to give a new proposal
system which matching and extraction are
performed.
 Database
 Images
 Encoder
 Decoder
The Process (Mechanism) of this Work; The process steps of the Technique/system
14

Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
SRM: In this image is segmented into parts.
This technique is fast and effective. N/A
2
AP: It is used to group similar regions together.
It is used for performing adaptive clustering. N/A
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Regional convolution mapping feature Convolutional neural networks
Annotate images User perception.
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
Images are taken as
input in this module
Image retrieval is
done where features
are extracted and
matching is also
performed.
In RBIR system colour, shape and text are extracted
for representing the region. RCMF is purposed for
low-level visual features. CNN helps in extracting
information of an image that is intrinsic image
through continuous layers.
It lacks in image segmentation accuracy for
complicated backgrounds. Also, it is required to
integrate learning methods attributes and the
components of metric learning.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
In this Region-based Image Retrieval method is
explained for matching region-to-region which is
grounded on only areas and matching image-to-
In this query image is processed and at final
outcome we get query results. In this matching and
extraction is performed.
This solution helped in image retrieval process by
matching features and extraction. Also with the
help of SRM-AP method merged regions are
15

image that uses all the areas. In region-to-region
identical, artificial selection is used which brings
more work so it is difficult to select the regions. It
causes loss of information whereas in Image-to-
image matching it is partially removed (Meng. et.
al., 2018).
obtained. And these merged regions reduce the
semantic gap.
Diagram/Flowchart
16

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Figure: Algorithm of the method.
17

5
Reference in APA format that will be in
'Reference List'
Rahim, N., Ahmad, J., Muhammad, K., Sangaiah, A. K., & Baik, S. W. (2018). Privacy-Preserving Image
Retrieval for Mobile Devices with Deep Features on the Cloud. Computer Communications.
Citation that will be in the content (Rahim. et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/pii/
S0140366417311064
Level of Journal: Q1 Cloud, Privacy preservation, Artificial intelligence, Features
extraction, Deep features, Image retrieval
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that need to be solved
What are the components of it?
Technique/Algorithm name:
Secret keys initialization
Pseudo keys
Tools:
Flicker
iCloud by Apple
Amazon Cloud Drive
Applied Area:
Mobile devices
Problem:
The major problem is of managing large data
on a device that is resource-constrained.
Goal:
The goal is to eliminate and extract the feature
of resources in of the devices.
Encoder
Decoder
Images
Database
18

The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
Image encryption: In this secret key is
generated and encryption is performed.
Key helps in improving privacy. N/A
2
Features extraction: In this classification and
feature extraction is done.
Features of images are maintained. N/A
3
CNN architecture: It helps in increasing the
accuracy.
The work is more precise. N/A
4
Autoencoder: In this encoding and decoding is
done.
It helps in reducing the deviation. N/A
5
Image indexing: In this image is retrieval is
done.
It is easily done with help of cloud and
smartphones.
N/A
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Content Cloud computing
Homomorphic technique High computation
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Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
Data or information
that need to be
secured.
This data is preserved
and make data more
secure and authentic.
This technique and framework help in ensuring the
privacy of user’s data and provide efficient
encryption method for better security and execution
time.
It lacks in security and process of retrieval can be
improved. Also, the data should be more
authentic.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
The solution is to by using cloud-based services
as it has many advantages. Like the ubiquitous
network, on-demand self-service, access, usage-
based pricing location independent resource
pooling, rapid resource elasticity.
The end users are individuals. With these
encryption techniques security increases and
features are extracted.
One natural solution to handle such problem might
be adopting cloud-based services due to its
tremendous advantages, such as on-demand self-
service, ubiquitous network access, location
independent resource pooling, rapid resource
elasticity, usage-based pricing and transference of
risk
Diagram/Flowchart
20

Figure: Image retrieval process
21

Version 2.0 _ Week 2 (10 Papers = 5 NEW Journal Papers + Updated previous 5 papers that you worked on them in Version 1.0)
6
Reference in APA format that will be in
'Reference List'
Xu, Y., Gong, J., Xiong, L., Xu, Z., Wang, J., & Shi, Y. Q. (2017). A privacy-preserving content-based image
retrieval method in cloud environment. Journal of Visual Communication and Image Representation, 43, 164-
172.
The citation that will be in the content (Xu, et. al., 2017)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/pii/
S104732031730007X
Level of Journal: Q1 Orthogonal decomposition, Privacy-preserving, Secure
search, Image retrieval
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Image retrieval process via deterministic
encryption
Tools:
Database, Image retrieval system,
Applied Area:
Engineering, Medical, Advertisement,
Fashion
Problem:
The major problem is of storing and sharing
the result when working with large amount of
data.
Goal:
The goal is to preserve the privacy of the
image retrieving process using orthogonal
decomposition.
 Image Database
 User interface
 Secure text search
 Watermarking technique
The Process (Mechanism) of this Work; The process steps of the Technique/system
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Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Organizing data-
On the data operations are performed.
It is used for improving the efficiency. N/A
2
Orthogonal decomposition-
DWT is used for both functional and numerical
analysis.
DWT is used in capturing frequency and
information of location.
N/A
3
Encryption-
Convert images to codes so that no unauthorized
user can access.
Used for increasing the security. It is computationally expensive.
4 Searching image-
In this images are searched by comparing feature
extraction and comparing similarity.
Used for fast calculation and fast image
retrieval.
N/A
5
Orthogonal composition and post-processing
of data-
In this encrypted data is calculated by inverse
orthogonal transform. Further by entropy
encoding encrypted image is constructed.
It helps in retrieving the secure data for using. N/A
Validation Criteria (Measurement Criteria)
23

Dependent Variable Independent Variable
Image retrieval orthogonal decomposition
Ciphertext scanning method streaming cipher
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
 Large
database with
images
 At output,
images are
retrieved,
searched,
browsed from
large with
maintaining
the privacy.
With different methods, images are retrieved from
the database by not affecting the data privacy. Also,
these methods have no restrictions while using
encryption algorithms and are purposed universally.
So it can use different applications.
Using different methods for image retrieval but
still, they lack accuracy. Also, the loss of
information is there.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
Xu, et. Al., (2017) purposed a content-based
image retrieval method used for maintaining
privacy. It is used in a cloud environment. In this,
an image is broken into two components of
orthogonal fields. Hence encryption and
extracting features are treated differently.
Image retrieval process is used in almost all the
fields. Here the method for increasing the privacy
has been purposed. Such as different encryption
techniques etc. also it improves the performance of
image retrieving.
This research helps in increasing the computing
capacity, efficiency and improves the retrieval
performance. Also improves the privacy.
Diagram/Flowchart
24

Figure: System model for purposed method.
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7
Reference in APA format that will be in
'Reference List'
Dong, Z., Jing, C., Pei, M., & Jia, Y. (2018). Deep CNN based binary hash video representations for face
retrieval. Pattern Recognition, 81, 357-369.
The citation that will be in the content (Dong, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/pii/
S003132031830147X
Level of journal: Q1 Deep CNN, Face video retrieval, Hash learning, Cross-
domain face retrieval
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Hashing for Face video retrieval method
Tools:
convolutional neural network
Applied Area:
Face detection, Face reorganization, Face
alignment, Face attribute prediction
Problem:
There are two major problems of face video
retrievals. First is large face variations of intra-
class and second is demands for conservation
of time.
Goal:
The goal is to develop a deep convolutional
neural network for face retrieval process using
binary hash video representations.
 Datasets
 Binary codes
 Video data
 AI
The Process (Mechanism) of this Work; The process steps of the Technique/system
26

Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Learn feature extractor-
In this, it learns hash functions and faces feature
extractor.
It helps in the compact hash representation of
video of faces for retrieval process.
N/A
2 Learn hash function-
In low-rank discriminative binary hashing
method, it helps in encouraging discriminative
power and stabilizing hash codes.
It helps in initializing hash functions. N/A
3 Fine tune-
In this approach, it achieves good performance
for face retrieval process on the two challenges
faced by datasets of TV-series.
This approach helps in good hashing results. N/A
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Hashing methods Neural networks
Face image retrieval Quantization error
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View) In this deep CNN for face retrieval is explained. In The major limitation is the performance of image
27

In this, the input is
facing frame.
And the output is
binary hash frame
representation.
network both the features of hash learning and
extraction is there. The feature extractor is used
with hashing.
retrieval is poor. Also, the hash codes are in large
number.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
Dong, et. al., (2018), purposed deep CNN for
learning binary hash video representation for the
purpose of face retrieval. Both face features and
hash functions are integrated for making two
components compatible. In this input is face
frame and output is the binary hash frame.
This is used to face retrieval task. In this different
types of methods are used for enhancing the
performance of retrieval task.
The low-rank discriminative binary hashing helps
in getting better starting network for learning hash
function. Also, it helps in improving the
performance of face video retrieval with the help
of fine-tuning the network.
Diagram/Flowchart
28

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Figure: depicted face retrieval system
29

Figure: Overview of face retrieval approach
30

8
Reference in APA format that will be in
'Reference List'
Huang, F., Jin, C., Zhang, Y., Weng, K., Zhang, T., & Fan, W. (2018). Sketch-based image retrieval with deep
visual semantic descriptor. Pattern Recognition, 76, 537-548.
The citation that will be in the content (Huang, et. at., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/pii/
S0031320317304867
Level of journal: Q1 Sketch-like transformation, Sketch-based image retrieval
(SBIR), Deep learning, Deep visual semantic descriptor,
Accelerated hierarchical K-means clustering, Multiple feature
fusion, Re-ranking optimization
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Sketch-based Image Retrieval
Accelerated hierarchical K-means
clustering
Tools:
Deep learning
Applied Area:
Artists for 3D modeling
Problem:
The major problem is to integrate different
information sources in SBIR for large-scale
annotated images.
Goal:
The goal is to enhance the SBIR with the help
of optimization techniques and deep visual
semantic descriptor.
 Image dataset
 Scanner
 Deep learning
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The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
Sketch-like transformation-
Images and sketches have different
representation and are rich and have more
information.
Helps in better sketch-image matching. N/A
2
Feature representation-
In this features are generated for sketches and
images.
It gives the advantage of visual descriptors and
helps in encoding information of features.
N/A
3
Re-ranking optimization-
In this, the plain Euclidean distance between
sketches and images are obtained.
Helps inaccurate sketch-image. High computational cost.
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Content-based Image Retrieval RGB images
Text-based Image Retrieval Plain text
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View) It helps in improving mutual resemblance. Also The major limitation is that only annotation tag of
32

Input is obtained from
contour detection of
an original image I.
In output contour
map GP if the original
image I am obtained.
builds a bridge between deep representation gap
and visual appearance gap.
image is only exploited for information. This
method is effective but affluent semantic
information is more obtained from annotated
images.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
In this features like deep visual semantic
descriptor and SBIR for re-ranking optimization
is developed. Also, deep learning pattern is
purposed which helps in encoding both low and
high-level features of sketches. Re-ranking
optimization adjusts the similarity of mages in the
ranking list (Huang, et. at., 2018).
In this, the approached method is used by
optimization techniques and also by deep visual
semantic descriptor for SBIR for easily integrating
different information.
It helps in enabling deep sketch-image matching.
Also, it is a reasonable method for mixing low-
level features with high-level features.
Diagram/Flowchart
Figure: Framework for enhanced SBIR model.
33

9
Reference in APA format that will be in
'Reference List'
Ha, I., Kim, H., Park, S., & Kim, H. (2018). Image retrieval using BIM and features from pretrained VGG
network for indoor localization. Building and Environment, 140, 23-31.
The citation that will be in the content (Ha, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/pii/
S0360132318302865
Level of journal: Q1 Feature extraction, Image-based indoor localization, Global
descriptor, Cross-domain image retrieval, BIM
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Convolutional neural networks (CNN)
Building information modelling (BIM)
Tools:
Image classification, Global descriptor
Applied Area:
Indoor localization
Problem:
The indoor information is lacking so it is hard
to detect the user orientation and position.
Goal:
The goal is to search for indoor photographs
images, particularly from the dataset. Also
with the help of BIM dataset of images are
built.
 BIM
 Global descriptor
 Database
The Process (Mechanism) of this Work; The process steps of the Technique/system
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Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 BIM model and rendering view images-
It gives the rendered view of images.
It is further used for design state. BIM image lack completeness.
2 Rendered BIM images with query images-
It contains a new image dataset that is further
sent to retrained VGG network.
It is used in prebuilt indoor environment. N/A
3 Feature extraction-
It is used for evaluating similarity of two
different images.
It helps in giving excellent performance in
various different computer field.
N/A
4 Result-
In result the most similar image is obtained.
Indoor location estimation is done. N/A
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
CNN Datasets
Image retrieval Pooling layers
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
A 2D raw image is
taken as in initial
Output is at
convolutional layer
The vision-based indoor localization has a high
performance for retrieving the image. Also with the
help of VGG features are extracted for finding
similarity among images. And the VGG layer is
The limitation is that GNSS is received with
difficulty in indoor environments as these are for
outdoor localization.
35

input. and further, it is taken
as input and also
known as feature map.
used for cross-domain image retrieval.
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
In this, it is purposed that for extracting the
features CNN (convolutional neural network) is
used in cross-domain image retrieval. And these
convolutional neural networks are majorly used in
the deep learning network. This neural network is
important for image processing also (Ha, et. al.,
2018).
It is used by engineers for the purpose of
construction of architectures using indoor
localization techniques for finding orientation and
position properly.
It helps in saving time and cost both and is better
than better other image-based indoor localization
methods.
Diagram/Flowchart
36

Figure: Image-based indoor localization method framework
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10
Reference in APA format that will be in
'Reference List'
Li, Y., Kong, X., Fu, H., & Tian, Q. (2018). Aggregating Hierarchical Binary Activations for Image
Retrieval. Neurocomputing.
The citation that will be in the content (Li, et. al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/pii/
S0925231218307501#sec0003
Level of journal: Q1 Bag-of-Words, Feature fusion, Image retrieval, Re-ranking,
Diffusion process
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Multi-layer Fusion, deep binary codes
Tools:
Convolutional Neural Networks
Applied Area:
Multimedia community
Problem:
In the existing image retrieval process, the
representation power of Convolutional Neural
Networks is lacking.
Goal:
The goal is to improve the CNNs
representation by using Multi-layer Fusion
(MF) approach with deep activations for
retrieving images.
 Convolutional Neural Networks
 Binary codes
The Process (Mechanism) of this Work; The process steps of the Technique/system
38

Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
Hierarchical binary activations-
It helps in giving a comparable performance and
explains about discriminative ability.
Helps in reducing memory cost. N/A
2
Multi-layer score fusion-
Major work to explore CNN based retrieval.
In this, all the activations are obtained
efficiently.
It still lacks in competitive performance.
3 Diffusion process with re-ranking-
It tells about the intrinsic relationship between
different objects.
It helps in benefiting fusion results. N/A
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
VLAD pooling Multi-scale Order less Pooling
CNN features Neural codes
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
In this image, patches
are taken as inputs.
In output, features are
obtained from every
layer for a single
image.
This approach helps in diffusion process and the
similarity relationship between images are more
precisely and smoothly adjusted with respect to
defined rules.
The developed CNNs are expected to be more
powerful and also deep models should be much
improved. MF needs improvements in future.
39

(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
In this, a multi-layer fusion approach is
formulated for the purpose of aggregating of deep
activations for retrieving images (Li, et. al.,
2018).
It is used for searching images from the database.
Hence the retiring performance can be improved
due to high-level semantics and integrating low-
level cues.
It is used for improving the CNN’S representation
power. Also, the MF algorithm is extended with
using features like SIFT. So this helps in achieving
competitive performance.
Diagram/Flowchart
Figure: The pipeline of purposed approach.
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The citation that will be in the content
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Tools:
Applied Area:
Problem:
Goal:
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
41

2
3
4
5
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
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42

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The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Tools:
Problem:
Goal:
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The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
2
3
4
5
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Input and Output Critical Thinking: Feature of this work, and Critical Thinking: Limitations of the research
44

Why (Justify) current solution, and Why (Justify)
Input (Data) Output (View)
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
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The Name of the Current Solution
(Technique/ Method/ Scheme/
The Goal (Objective) of this Solution & What are the components of it?
45

Algorithm/ Model/ Tool/ Framework/ ...
etc )
What is the Problem that needs to be solved
Technique/Algorithm name:
Tools:
Applied Area:
Problem:
Goal:
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
2
3
4
5
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
46

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URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Tools:
Applied Area:
Problem:
Goal:
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
2
3
4
5
Validation Criteria (Measurement Criteria)
48

Dependent Variable Independent Variable
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
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Algorithm/ Model/ Tool/ Framework/ ...
etc )
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What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Tools:
Applied Area:
Problem:
Goal:
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
50

1
2
3
4
5
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
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Tools:
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Goal:
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Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
2
3
4
5
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
53

Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
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54

The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Tools:
Applied Area:
Problem:
Goal:
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
2
3
4
5
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
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What is the Problem that needs to be solved
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Tools:
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Problem:
Goal:
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1
2
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3
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What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Tools:
Applied Area:
Problem:
Goal:
59

The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1
2
3
4
5
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3
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What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name:
Tools:
Applied Area:
Problem:
Goal:
The Process (Mechanism) of this Work; The process steps of the Technique/system
82

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(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
Diagram/Flowchart
30
Reference in APA format that will be in
'Reference List'
The citation that will be in the content
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/ ...
etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be solved
What are the components of it?
Technique/Algorithm name: Problem:
84

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Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the research
current solution, and Why (Justify)
Input (Data) Output (View)
(Describe the research/current solution) Evaluation Criteria How this research/current solution is valuable
for your project
Diagram/Flowchart
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