Semi-supervised distance metric learning for collaborative image retrieval

• Main Authors: HOI, Steven, LIU, Wei, CHANG, Shih-Fu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2008
• Subjects: content-based retrieval; graph theory; groupware; image retrieval; learning (artificial intelligence); relevance feedback; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2381; https://ink.library.smu.edu.sg/context/sis_research/article/3381/viewcontent/CVPR08_ssml.pdf
• Institution: Singapore Management University

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.