Abstract
A novel Matrix Modular Support Vector Machine(MMSVM) classifier is proposed to partition a visual concept problem into many easier two-class problems.This MMSVM shows significant detection improvements on the ImageClef2008 VCDT task, with a relative reduction of 15% of the classification error, compared with usual SVMs.
Work supported by French National Agency of Research ANR-06-MDCA-002, and Research Fund for the Doctoral Program of Higher Education of China 200803591024.
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Zhao, ZQ., Glotin, H. (2009). Enhancing Visual Concept Detection by a Novel Matrix Modular Scheme on SVM. In: Peters, C., et al. Evaluating Systems for Multilingual and Multimodal Information Access. CLEF 2008. Lecture Notes in Computer Science, vol 5706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04447-2_80
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DOI: https://doi.org/10.1007/978-3-642-04447-2_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04446-5
Online ISBN: 978-3-642-04447-2
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