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A Unified Optimization Based Learning Method for Image Retrieval

11 years 6 months ago
A Unified Optimization Based Learning Method for Image Retrieval
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 in the senses that 1) it makes use of the information from various aspects in a global optimization manner so that the retrieval performance might be maximally improved; 2) it provides a natural way to support two typical query scenarios in image retrieval. The proposed method has a solid mathematical ground. Systematic experimental results on a general-purpose image database demonstrate that it achieves significant improvements over existing methods.
Hanghang Tong, Jingrui He, Mingjing Li, Wei-Ying M
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Hanghang Tong, Jingrui He, Mingjing Li, Wei-Ying Ma, Changshui Zhang, HongJiang Zhang
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