DocumentCode :
3549101
Title :
A unified optimization based learning method for image retrieval
Author :
Hanghang Tong ; Jingrui He ; Mingjing Li ; Wei-Ying Ma
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Volume :
2
fYear :
2005
fDate :
20-25 June 2005
Firstpage :
230
Abstract :
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.
Keywords :
image retrieval; iterative methods; learning (artificial intelligence); relevance feedback; visual databases; general-purpose image database; graph model; image retrieval; iterative form; offline semantic information; online relevance feedback; optimization based learning method; query processing; Asia; Content based retrieval; Feedback; Image databases; Image retrieval; Information retrieval; Learning systems; Optimization methods; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2372-2
Type :
conf
DOI :
10.1109/CVPR.2005.54
Filename :
1467447
Link To Document :
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