TsoKaDo

Abstract

In this paper, a new visually based query expansion method for image retrieval from distributed web search engines is proposed. The innovation of this method lies in the fact that the effectiveness of image retrieval is improved because it is based on recursive query recommendation. Until now, the problem of retrieving images, very close to the seeker’s desires from web search engines, is growing as well as the continuous increase of confusing image information over the Internet. The method, described in this paper, proposes a web image search engine, called TsoKaDo, which at first, given a user’s query, parses images from the three most known search engines, e.g. Google, Bing and Ask. Then, it classifies them in C automatically computed classes using Content Based techniques and more specific the Color and Edge Directivity Descriptor (CEDD). Consequently, the most representative image of each class is compared, using CEDD again, with the results parsed from Flickr, searched with the same keyword. Finally the tags of the top-K images of Flickr are classified based on their semantic distance, and are proposed to the user in order to expand his query for better results.

Please note that this web application is an undergraduate project!!!!!!

Another project from the Duth Robotics Team

The paper is currently under review. I ‘ll post more details ASAP!!!

Lazaros T. Tsochatzidis, Athanasios C. Kapoutsis, Nikolaos I. Dourvas, Savvas A. Chatzichristofis, Yiannis S. Boutalis, “QUERY EXPANSION BASED ON VISUAL IMAGE CONTENT”, «5th Panhellenic Scientific Conference for Undergraduate and Postgraduate Students in Computer Engineering, Informatics, related Technologies and Applications», September 30 to October 1, 2011, Kastoria, Greece, Submitted for Publication

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