A Survey of Landmark Recognition Using the Bag-of-Words Framework

Recent years have seen an exponential increase in the use of mobile devices. Since many of the mobile devices are equipped with a camera and are connected to the internet, localization in an urban environment using landmark images is gaining popularity. The idea is simple. A tourist takes images of a landmark where he or she is standing with a mobile camera which are then transmitted to a server where the image(s) are matched against a database of landmark images for that locality. If a match is found, relevant information such as background information on the landmark, nearby transit facilities or information on other important landmarks nearby is sent back. This type of application has tremendous potential as a mobile city guide or navigation aid. In this paper, we investigate the use of local invariant shape features and global features such as colour and texture for the recognition task as evident from literature and present various retrieval techniques. A variety of descriptors for landmark recognition and scene classification are discussed. Insights into vocabulary building and weighting schemes for representing landmark images are provided that can help in boosting recognition rates.