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Title: An Empirical Study Of Multi-Label Learning Methods For Video Annotation
Author(s): A. Dimou, G. Tsoumakas, V. Mezaris, I. Kompatsiaris, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (6 pages).
Appeared in: 7th International Workshop on Content-Based Multimedia Indexing, IEEE, Chania, Crete, 2009.
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 Mediamill 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.
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