Image Retrieval Method Based on Selected Relation Embedding Algorithm
LIU Li1, TAO Dan2, PENG Gang1
1.Department of Computer Science,Huizhou University,Huizhou 516007 2.School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044
Abstract The key of image retrieval based on manifold learning and relevance feedback is to learn user semantics from a few feedbacks based on the low-level visual information, to get semantic subspace manifold. To get more real semantic subspace, the low-level visualization and the user feedback information are differentiated, and the relations of inter-class and intra-class are learned selectively from feedback information based on visualization feature. Then, a selected relation embedding algorithm for image retrieval is proposed. By this algorithm, more realistic semantic manifold structures are kept, and the retrieval precision in low-dimensional space is enhanced. The experimental results show that the proposed method mapps the image into a wider range of low-dimensional space, and improves the retrieval precision up to 16.3% after two feedback iterations.
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2012, Vol. 25 
