A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Modern Information Retrieval
ImageRover: A Content-Based Image Browser for the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Cortina: a system for large-scale, content-based web image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Optimal multimodal fusion for multimedia data analysis
Proceedings of the 12th annual ACM international conference on Multimedia
Efficient propagation for face annotation in family albums
Proceedings of the 12th annual ACM international conference on Multimedia
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Time-dependent semantic similarity measure of queries using historical click-through data
Proceedings of the 15th international conference on World Wide Web
A unified framework for image retrieval using keyword and visual features
IEEE Transactions on Image Processing
Evaluating implicit judgments from image search clickthrough data
Journal of the American Society for Information Science and Technology
Ontology-based personalised retrieval in support of reminiscence
Knowledge-Based Systems
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Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalability, efficiency and effectiveness issues. In this paper we proposed a scalable relevance feedback mechanism using click-through data for web image retrieval. The proposed mechanism regards users' click-through data as implicit feedback which could be collected at lower cost, in larger quantities and without extra burden on the user. During RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Experimental results on a database consisting of nearly three million Web images show that the proposed mechanism is wieldy, scalable and effective.