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Similarity learning via dissimilarity space in CBIR

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Similarity learning via dissimilarity space in CBIR
In this paper, we introduce a new approach to learn dissimilarity for interactive search in content based image retrieval. In literature, dissimilarity is often learned via the feature space by feature selection, feature weighting or a parameterized function of the features. Different from existing techniques, we use relevance feedback to adjust dissimilarity in a dissimilarity space. To create a dissimilarity space, we use Pekalska’s method [15]. After the user gives feedback, we apply active learning with one-class SVM on this space. Results on a Corel dataset of 10000 images and a TrecVid collection of 43907 keyframes show that our proposed approach can improve the retrieval performance over the feature space based approach. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: [Search process, Relevance feedback] General Terms Experimentation, Algorithm Keywords Dissimilarity learning, interactive search, visualization
Giang P. Nguyen, Marcel Worring, Arnold W. M. Smeu
Added 14 Jun 2010
Updated 14 Jun 2010
Type Conference
Year 2006
Where MIR
Authors Giang P. Nguyen, Marcel Worring, Arnold W. M. Smeulders
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