Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Image annotation via graph learning
Pattern Recognition
Learning semantic distance from community-tagged media collection
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
The power of convex relaxation: near-optimal matrix completion
IEEE Transactions on Information Theory
Image annotation using multi-correlation probabilistic matrix factorization
Proceedings of the international conference on Multimedia
Mining social images with distance metric learning for automated image tagging
Proceedings of the fourth ACM international conference on Web search and data mining
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
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With the popularity of social media applications, large amounts of social images associated with rich context are available, which is helpful for many applications. In this paper, we propose a Low Rank distance Metric Learning (LRML) algorithm by discovering knowledge from these rich contextual data, to boost the performance of CBIR. Different from traditional approaches that often use the must-links and cannot-links between images, the proposed method exploits information from the visual and textual domains. We assume that the visual similarity estimated by the learned metric is expected to be consistent with the semantic similarity in the textual domain. Since tags are usually noisy, misspelling or meaningless, we also leverage the preservation of visual structure to prevent overfitting those noisy tags. On the other hand, the metric is straightforward constrained to be low rank. We formulate it as a convex optimization problem with nuclear norm minimization and propose an effective optimization algorithm based on proximal gradient method. With the learned metric for image retrieval, some experimental evaluations on a real-world dataset demonstrate the outperformance of our approach over other related work.