Automatic background substitution using monocular camera and temporal foreground probability model
Proceedings of the 2nd international conference on Ubiquitous information management and communication
A novel region-based image retrieval algorithm using selective visual attention model
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
A novel graph kernel based SVM algorithm for image semantic retrieval
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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In region-based image retrieval, not all the regions are important for retrieving similar images and rather, the user is often interested in performing a query on only salient regions. Therefore, we propose a new method for extraction of salient regions using Support Vector Machines (SVM) and a method for importance score learning according to the user's interaction. Once an image is segmented, our algorithm permits the Attention Window (AW) according to the variation of an image and selects salient regions by using the pre-defined feature vector and SVM within the AW. By using SVM, we do not need to determine the heuristic feature parameters and produce more reasonable results. The distance values from SVM are used for initial importance scores of salient regions and our proposed updating algorithm using relevance feedback updates them automatically. Through performance comparison with parametric salient extraction method, our proposed method shows better performance as well as semantic query interface for object-level image retrieval.