Intermediate Semantics Based Distance Metric Learning For Video Annotation And Similarity Measurements

The similarity metric between videos is integral to several key tasks, including video retrieval, classification and recommendation. Since there is no standard criterion for the similarity measurement between videos except measuring manually, it is difficult to collect large training dataset for distance metric learning algorithms. Moreover, the existing distance metric learning (DML) methods for multimedia data suffer from two critical limitations: (1) they typically attempt to learn a distance function on the single label setting, in which each item is only labeled with single label; (2) they are often designed for learning distance metrics on low-level features, which ignore the semantic similarity of the multimedia data. To address these problems, in this paper, we propose a novel framework of Intermediate Semantics based Distance Learning (ISDL) for video clips, which aims to integrate semantics of multiple modals optimally for distance metric learning. In particular, the proposed framework: (1) generates the training pairs automatically; (2) defines multi-modal concepts for similarity measure among videos; (3) learns the distance metric for video clips based on the intermediate semantics. We conduct an extensive set of experiments to evaluate the performance of the proposed algorithms, and the results validate the effectiveness of our proposed approach.