Abstract
In this paper, a relevance feedback scheme for both keyword and visual feature-based image retrieval is proposed. For each keyword, a statistical model is trained offline based on visual features of a small set of manually labeled images and used to propagate the keyword to other unlabeled ones. Besides the offline model, another model is constructed online using the user provided positive and negative images as training set. Support vector machines (SVMs) in the binary setting are adopted as both offline and online models. To effectively combine the two models, a multi-model query refinement algorithm is introduced. Furthermore, an entropy-based active learning strategy is proposed to improve the efficiency of relevance feedback process. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness of the proposed relevance feedback scheme.
This work was performed at Microsoft Research Asia. Feng Jing and Bo Zhang are supported in part by NSF Grant CDA 96-24396.
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Jing, F., Li, M., Zhang, HJ., Zhang, B. (2004). Relevance Feedback for Keyword and Visual Feature-Based Image Retrieval. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds) Image and Video Retrieval. CIVR 2004. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27814-6_52
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DOI: https://doi.org/10.1007/978-3-540-27814-6_52
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