A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
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
iFind—a system for semantics and feature based image retrieval over Internet
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A statistical correlation model for image retrieval
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
FeedbackBypass: A New Approach to Interactive Similarity Query Processing
Proceedings of the 27th International Conference on Very Large Data Bases
IEEE Transactions on Image Processing
Long-term learning of semantic grouping from relevance-feedback
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Context-Based image similarity queries
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
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We propose a novel approach to memorizing content relevance information accumulated in relevance feedback sessions (historical relevance feedback) in image retrieval. It is based on the idea that by storing identification numbers of several representative images for each positive image after a query session, it is possible to provide more precise and diverse retrieval results when this image is used later as a query in a new search session. To do so, a criterion for a "good" buddy image is proposed. Based on such criterion, an algorithm is designed to maximize the space spanned by the selected buddy images. Since the proposed approach requires only a constant storage space for each image, it has good scalability for a large size of image database. Experimental results on a database of 10,000 images show the high efficiency and good scalability of the proposed memorization scheme.