Web Image Retrieval Re-Ranking with Relevance Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Region-Based Image Retrieval with High-Level Semantic Color Names
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Region-Based image retrieval with perceptual colors
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
A review on automatic image annotation techniques
Pattern Recognition
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Existing Web image search engines index images by textual descriptions including filename, image caption, surrounding text, etc. However, the textual description available on the Web could be ambiguous or inaccurate in describing the actual image content and some images irrelevant to user's query are also returned by text-based search engines. In this paper, we propose to integrate the existing text-based image search engine with visual features, in order to improve the performance of pure text-based Web image search. The proposed algorithm is named SIEVE. Practical fusion methods are proposed to integrate SIEVE with contemporary text-based search engines. In our approach, text-based image search results for a given query are obtained first. Then, SIEVE is used to filter out those images which are semantically irrelevant to the query. Experimental results show that the image retrieval performance using SIEVE improves over Google image search significantly.