Image Recognition and Landmark Detection: A Literature Review
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Added on 2019-09-20
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This paper conducts a literature review on image recognition and landmark detection, covering topics such as feature extraction, image processing, visual search architectures, and image search optimization. The goal is to create a real-time landmark recognition Android app that can simplify content understanding and geolocation of images returned via GPS.
Image Recognition and Landmark Detection: A Literature Review
Added on 2019-09-20
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AbstractEssentially, computer vision is the computer's ability to gain a high-level understanding from text, digital images or videos that it is presented with. Within the arena of machine learning, computer visualisation and image processing, image recognition presents a broad horizon of challenging tasks. How to extract optimal, representative key features that can reflect the intrinsic content of an image as accurately and efficiently as possible remains both a primary interest and an exacting task within the domain of computer vision. Keywords: - Image Recognition, Machine Learning, Data-set, Bag-of-Words, GPS Receiver, Key Point Detection/Description, PICASA, Monulens and Google Goggles.1
Table of Contents1.0 Introduction......................................................................................................................42.0 Literature Review: ...........................................................................................................62.1 A Survey of Major Image/Landmark Recognition & Comparison Techniques:..............62.2 Mobile Visual Search Architectures:.............................................................................113.0 Summary........................................................................................................................184.0 Conclusion.....................................................................................................................195.0 References.....................................................................................................................206.0 Bibliography...................................................................................................................24
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1.0 IntroductionA touristic landmark is an instantly recognisable building or site (such as a monument or a cathedral).As a traveller, gathering real-time, interactive andrelevant information about a landmark or monument andits surrounding areas of interest are important aspects ofan individual's journey because of the cultural and historical aspects it can present. The World Wide Web is readily abundant with imagesand video recordings.Creating a mobile application which has the ability torecognise and match a vast number of landmarksefficiently still remains a challenge due to the sheer number of images that are required to be searched within a database/dataset, along with the presence of possible visual distortions such as external litter, illuminated amendment and dynamic geometry of the imaging devices by which the visual media was recorded (Rekhansh et al., 2015). However, with the vast amount of landmark pictures emerging within the World Wide Web and the advancement of landmark picture sharing via websites such as Picasa and Flickr, the requirement for a computer conception to recognise landmarks universally through the creation of reliable image identification engines and algorithms is necessary (Rohr, 2010). In this research work, a literature review will be conducted to assess research developments in the field of image recognition and the discoveries and progress that has been made towards technologies such as image feature extraction, image processing, visual search architectures and image search optimisation in general.Finally, this paper will be summarised, with primary conclusions highlighted and some potential research directions and techniques that can be further explored and improved upon identified. 4Fig.1: Famous Landmarks (Google.com, 2016)
Only through this process may an application be bestowed with image recognition capabilities advanced enough to fully recognise and understand a diverse range of imagesusing neural networks (Parker, 1996).This includes taking into consideration a number of issues that often present themselves when creating such a device, such as the variations that are guaranteed to emerge in any given landmark from one observation to the next (Yairi, Hirama, and Hori, 2003).The ultimate goal of the proposed project is to create a real-time landmark recognition Android app.The device will use a large dataset of images which will be stored on the device itself for matching. By identifying a given landmark, the application will simplify both content understanding and the geolocation of images returned via GPS, enabling a topographical representation and navigation of landmarks in the local area to provide appropriate tour guide suggestions and guidance via online resources such as Yelp and Google Maps.However, as already highlighted, efficiency is a challenge for such a potentially unstable, large-scale image recognition system (Pinto, Cox and DiCarlo, 2008).
2.0 Literature Review: 2.1 A Survey of Major Image/Landmark Recognition & Comparison Techniques:Various works have been carried out within the area of image recognition adhering to the requirements of the main specifications.On observing the dearth of methods for the identification and correlation of specific details from images stored on the World Wide Web and the limits of purely camera-based, contemporary programs, various researchers have attempted to come up with a means of landmark recognition and modelling (some on a global-scale). Colios et al. (2001) experimented with automated landmark identification for robots by means of both projective and point-permutation invariant vectors. These vectors were used to identify landmark patterns based on workspace planar features, enabling the creation of direct, point-to-point correspondences in an indoor setting. This, in turn, allowed for the use of both projectivity constraints and the convex hull to identify matches in sets of five different images. There was a noticeable margin of error; though this was reduced through the use of sub-landmarks as outlier patterns. However, this approach was limited to indoor environments only, rendering it's effectiveness to be restrictive (Rekhansh et al., 2015).As part of a different project, vision-based landmark recognition was also used by Mata et al. (2002) to perform topological localisation of mobile robots. Both natural and artificial landmarks alike were used here, enabling the creation of a search function whereby pattern identification/recognition techniques were applied to digital images. 6
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