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Semantic manifold learning for image retrieval

8 years 10 months ago
Semantic manifold learning for image retrieval
Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relations specified in the feedback. Given that, we propose an augmented relation embedding (ARE) to map the image space into a semantic manifold that faithfully grasps the user’s preferences. Besides ARE, we also look into the issues of selecting a good feature set for improving the retrieval performance. With these two aspects of efforts we have established a system that yields far better results than those previously reported. Overall, our approach can be characterized by three key properties: 1) The framework uses one relational graph to describe the similarity relations, and the other two to encode the relevant/irrelevant relations indicated in the feedback. 2) With the relational graphs so defined, learning a semantic manifold can be transformed into solving a constrained optimization problem, and is redu...
Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where MM
Authors Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
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