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Full-Text Articles in Physical Sciences and Mathematics
Semi-Supervised Svm Batch Mode Active Learning For Image Retrieval, Steven Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Semi-Supervised Svm Batch Mode Active Learning For Image Retrieval, Steven Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to …
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval, Steven Hoi, Wei Liu, Shih-Fu Chang
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval, Steven Hoi, Wei Liu, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
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 …