Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2008

Abstract

Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called “Collaborative Image Retrieval” (CIR). To effectively explore the log data, we propose a novel semi-supervised distance metric learning technique, called “Laplacian Regularized Metric Learning” (LRML), for learning robust distance metrics for CIR. Different from previous methods, the proposed LRML method integrates both log data and unlabeled data information through an effective graph regularization framework. We show that reliable metrics can be learned from real log data even they may be noisy and limited at the beginning stage of a CIR system. We conducted extensive evaluation to compare the proposed method with a large number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information.

Keywords

content-based retrieval, graph theory, groupware, image retrieval, learning (artificial intelligence), relevance feedback

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

CVPR 2008: IEEE Conference on Computer Vision and Pattern Recognition: 23-28 June 2008, Anchorage, AK: Proceedings

First Page

1

Last Page

7

ISBN

9781424422432

Identifier

10.1109/CVPR.2008.4587351

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/CVPR.2008.4587351

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