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| 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 |
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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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Received: 26 October 2011
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2012, Vol. 25 
