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
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Although relevance feedback (RF) has been extensively studied in the content-based image retrieval community, no commercial Web image search engines support RF because of scalability, efficiency, and effectiveness issues. In this paper, we propose a unified relevance feedback framework for Web image retrieval. Our framework shows advantage over traditional RF mechanisms in the following three aspects. First, during the 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. Second, the textual feature-based RF mechanism employs an effective search result clustering (SRC) algorithm to obtain salient phrases, based on which we could construct an accurate and low-dimensional textual space for the resulting Web images. Thus, we could integrate RF into Web image retrieval in a practical way. Last, a new user interface (UI) is proposed to support implicit RF. On the one hand, unlike traditional RF UI which enforces users to make explicit judgment on the results, the new UI regards the users' click-through data as implicit relevance feedback in order to release burden from the users. On the other hand, unlike traditional RF UI which hardily substitutes subsequent results for previous ones, a recommendation scheme is used to help the users better understand the feedback process and to mitigate the possible waiting caused by RF. Experimental results on a database consisting of nearly three million Web images show that the proposed framework is wieldy, scalable, and effective.