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In this paper, an optimization based learning method is proposed for image retrieval from graph model point of view. Firstly, image retrieval is formulated as a regularized optimization problem, which simultaneously considers the constraints from low-level feature, online relevance feedback and offline semantic information. Then, the global optimal solution is developed in both closed form and iterative form, providing that the latter converges to the former. The proposed method is unified indoi:10.1109/cvpr.2005.54 dblp:conf/cvpr/TongHLMZZ05 fatcat:idaigrtborhu3dn5uoku5meipu