Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval
Conference Proceeding Article
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.
content-based retrieval, graph theory, groupware, image retrieval, learning (artificial intelligence), relevance feedback
Computer Sciences | Databases and Information Systems
Data Management and Analytics
CVPR 2008: IEEE Conference on Computer Vision and Pattern Recognition: 23-28 June 2008, Anchorage, AK: Proceedings
City or Country
HOI, Steven; LIU, Wei; and CHANG, Shih-Fu.
Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval. (2008). CVPR 2008: IEEE Conference on Computer Vision and Pattern Recognition: 23-28 June 2008, Anchorage, AK: Proceedings. 1-7. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2381