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

• Type: Conference
• Year: 2006
• Where: MIR
• Authors: Giang P. Nguyen, Marcel Worring, Arnold W. M. Smeulders
• Keywords: Dissimilarity Space, Feature Space, MIR 2006, Multimedia, Relevance Feedback