An Empirical Study of Multi-label Learning Methods for Video Annotation

Abstract

This paper presents an experimental comparison of different approaches to learning from multi-labeled video data. We compare state-of-the-art multi-label learning methods on the Media mill Challenge dataset. We employ MPEG-7 and SIFT-based global image descriptors independently and in conjunction using variations of the stacking approach for their fusion. We evaluate the results comparing the different classifiers using both MPEG-7 and SIFT-based descriptors and their fusion. A variety of multi-label evaluation measures is used to explore advantages and disadvantages of the examined classifiers. Results give rise to interesting conclusions.