KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/344331
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AbstractRecently, a number of methods have been proposed to improve image retrieval accuracy by capturing context information. These methods try to compensate for the fact that a visually less similar image might be more relevant because it depicts the same object. We propose a new quick method for refining any pairwise distance metric, it works by iteratively discovering the object in the image from the most similar images, and then refine the distance metric accordingly. Test show that our technique improves over the state of art in terms of accuracy over the MPEG7 dataset.
CitationContextual Distance Refining for Image Retrieval 2014, 8 (1):40 The Open Cybernetics & Systemics Journal
SponsorsThis work was supported by Chongqing Key Laboratory of Computational Intelligence (Grant No. CQ-LCI-2013-02).
PublisherBentham Science Publishers Ltd.
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