Publication Type

Conference Proceeding Article

Publication Date

10-2008

Abstract

Distance metric learning has been widely investigated in machine learning and information retrieval. In this paper, we study a particular content-based image retrieval application of learning distance metrics from historical relevance feedback log data, which leads to a novel scenario called collaborative image retrieval. The log data provide the side information expressed as relevance judgements between image pairs. Exploiting the side information as well as inherent neighborhood structures among examples, we design a convex regularizer upon which a novel distance metric learning approach, named output regularized metric learning, is presented to tackle collaborative image retrieval. Different from previous distance metric methods, the proposed technique integrates synergistic information from both log data and unlabeled data through a regularization framework and pilots the desired metric toward the ideal output that satisfies pairwise constraints revealed by side information. The experiments on image retrieval tasks have been performed to validate the feasibility of the proposed distance metric technique.

Keywords

Distance Metric Learning, Side Information, Output Regularized Metric Learning, Collaborative Image Retrieval

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part III

Volume

5304

First Page

358

Last Page

371

ISBN

9783540886891

Identifier

10.1007/978-3-540-88690-7_27

Publisher

Springer Verlag

City or Country

Berlin

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1007/978-3-540-88690-7_27

Share

COinS