Open Access Journal of Scientific, Technology & Engineering Research

International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)

IJTEEE >> Volume 3 - Issue 8, August 2015 Edition

International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289

Geo-Location And Information Retrieval For On-Premise Signs

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Index Terms: Real-world objects, street view scenes, learning and recognition, object image data set.



Abstract: Image recognition has become an integral and important part of today’s technical world. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, in which comprises of OPS images of different businesses which are basically collected from Google’s Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. This approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications. This project provides very simple and modified features, which are very easy to view and operate. This project is designed and organized in a very simplified manner to hold the details of images and return the geo-location of the image.



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