Extended CBIR via learning semantics of query image

Authors:
Chuanghua Gui;Jing Liu;Changsheng Xu;Hanqing Lu
Affiliations:
Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Year:
2010

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Abstract

This demo presents a web image search engine via learning semantics of query image. Unlike traditional CBIR systems which search images according to visual similarities, our system implements an extended CBIR (ExCBIR) which returns both visually and semantically relevant images. Given a query image, we first automatically learn its semantic representation from those visual similar images, and then combine the semantic representation and their visual properties to output the searching result. Considering that different visual features have variously discriminative power under a certain semantic context, we give more confidence to the feature whose result images are more consistent on semantics. Experiments on a large-scale web images demonstrate the effectiveness of our system.