A unified optimization based learning method for image retrieval

Hanghang Tong, Jingrui He, Mingjing Li, Wei Ying Ma, Changshui Zhang, Hong Jiang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
EditorsC. Schmid, S. Soatto, C. Tomasi
Pages230-235
Number of pages6
Volume2
DOIs
StatePublished - 2005
Externally publishedYes
Event2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - San Diego, CA, United States
Duration: Jun 20 2005Jun 25 2005

Other

Other2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
CountryUnited States
CitySan Diego, CA
Period6/20/056/25/05

Fingerprint

Image retrieval
Global optimization
Semantics
Feedback

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Software
  • Control and Systems Engineering
  • Engineering(all)

Cite this

Tong, H., He, J., Li, M., Ma, W. Y., Zhang, C., & Zhang, H. J. (2005). A unified optimization based learning method for image retrieval. In C. Schmid, S. Soatto, & C. Tomasi (Eds.), Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2, pp. 230-235) https://doi.org/10.1109/CVPR.2005.54

A unified optimization based learning method for image retrieval. / Tong, Hanghang; He, Jingrui; Li, Mingjing; Ma, Wei Ying; Zhang, Changshui; Zhang, Hong Jiang.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. ed. / C. Schmid; S. Soatto; C. Tomasi. Vol. 2 2005. p. 230-235.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tong, H, He, J, Li, M, Ma, WY, Zhang, C & Zhang, HJ 2005, A unified optimization based learning method for image retrieval. in C Schmid, S Soatto & C Tomasi (eds), Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 2, pp. 230-235, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, CA, United States, 6/20/05. https://doi.org/10.1109/CVPR.2005.54
Tong H, He J, Li M, Ma WY, Zhang C, Zhang HJ. A unified optimization based learning method for image retrieval. In Schmid C, Soatto S, Tomasi C, editors, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2. 2005. p. 230-235 https://doi.org/10.1109/CVPR.2005.54
Tong, Hanghang ; He, Jingrui ; Li, Mingjing ; Ma, Wei Ying ; Zhang, Changshui ; Zhang, Hong Jiang. / A unified optimization based learning method for image retrieval. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. editor / C. Schmid ; S. Soatto ; C. Tomasi. Vol. 2 2005. pp. 230-235
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