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A New Multiple Kernel Approach for Visual Concept Learning

  • Jingjing Yang
  • Yuanning Li
  • Yonghong Tian
  • Lingyu Duan
  • Wen Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5371)

Abstract

In this paper, we present a novel multiple kernel method to learn the optimal classification function for visual concept. Although many carefully designed kernels have been proposed in the literature to measure the visual similarity, few works have been done on how these kernels really affect the learning performance. We propose a Per-Sample Based Multiple Kernel Learning method (PS-MKL) to investigate the discriminative power of each training sample in different basic kernel spaces. The optimal, sample-specific kernel is learned as a linear combination of a set of basic kernels, which leads to a convex optimization problem with a unique global optimum. As illustrated in the experiments on the Caltech 101 and the Wikipedia MM dataset, the proposed PS-MKL outperforms the traditional Multiple Kernel Learning methods (MKL) and achieves comparable results with the state-of-the-art methods of learning visual concepts.

Keywords

Visual Concept Learning Support Vector Machine Multiple Kernel Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jingjing Yang
    • 1
    • 2
    • 3
  • Yuanning Li
    • 1
    • 2
    • 3
  • Yonghong Tian
    • 3
  • Lingyu Duan
    • 3
  • Wen Gao
    • 1
    • 2
    • 3
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate SchoolChinese Academy of SciencesBeijingChina
  3. 3.The Institute of Digital Media, School of EE & CSPeking UniversityBeijingChina

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