Image Retrieval Based on Dominant Set Clustering and Support Vector Machine
WANG Man1, PENG Guo-Hua1, YE Zheng-Lin1, ZHAO Cong1, WANG Shu-Xun1,2
1.Department of Applied Mathematics, School of Science, Northwestern Polytechnical University, Xi'an 7100722.
Department of Mathematics, Shaanxi University of Technology, Hanzhong 723000
An unsupervised learning approach for content based image retrieval system is presented, which combines the low-level vision feature and the high-level semantics using the memorized SVM relevance feedback. The proposed approach fully explores the similarities among images in database by using the improved dominant set clustering to optimize the “relevance” feedback results from SVM. The experimental results show that the proposed method can be convergent to user's retrieval concept rapidly, and it has the superior precision and total relevance feedback times in image retrieval system.
王曼，彭国华，叶正麟，赵丛，王树勋. 基于优势集聚类和支持向量机的图像检索*[J]. 模式识别与人工智能, 2008, 21(5): 689-694.
WANG Man, PENG Guo-Hua, YE Zheng-Lin, ZHAO Cong, WANG Shu-Xun. Image Retrieval Based on Dominant Set Clustering and Support Vector Machine. , 2008, 21(5): 689-694.
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2008, Vol. 21 
