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...

• Type: Conference
• Year: 2005
• Where: MM
• Authors: Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
• Keywords: MM 2005, Relational Graphs, Semantic Manifold, Similarity Relations