Steven C.H. Hoi † , Wei Liu † , Michael R. Lyu † , Wei-Ying Ma ‡

1. The Chinese University of Hong Kong Learning Distance Metrics with Contextual Constraints for Image Retrieval Steven C.H. Hoi†, Wei Liu†, Michael R. Lyu†, Wei-Ying Ma‡ †The Chinese University of Hong Kong‡Microsoft Research Asia Motivations Contributions • Distance metrics are important for image retrieval. • Learning distance metrics with pairwise contextual constraints is critical to bridge the semantic gaps of image retrieval. • Traditional distance metric learning usually studies linear distance metrics, which may not be effective for image retrieval. • A new distance metric learning method is proposed for image retrieval. • We developed two algorithms, Discriminative Component Analysis (DCA) and Kernel DCA, for learning metrics with contextual constraints. • Empirical evaluations have been conducted for image retrieval. . METHODOLOGY DCA • Main Ideas • Improving Relevant Component Analysis (RCA) by combining the dissimilar contextual constraints. • Looking for the most discriminative transformation for learning the metrics. Covariance matrix between data chunks Covariance matrix within data chunks KERNEL DCA Learning the optimal transformation Experimental Results (a) Original Data Space (a) “Dogs” retrieval (b) “Butterfly” retrieval (c) “Roses” retrieval (b) Space via Kernel Experimental results on the 20-Cat dataset (c) Embedding Space via KDCA The proposed DCA and Kernel DCA are promising for learning distance metrics from contextual constraints for image retrieval. CoNCLUSION CUHK and Microsoft Research Asia IEEE Computer Vision and Pattern Recognition 2006