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Mixed-Norm Regression for Visual Classification

  • Xiaofeng Zhu
  • Jilian Zhang
  • Shichao Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)

Abstract

This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

Keywords

image classification multi-view classification sparse coding Structure sparsity Reproducing kernel Hilbert space 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaofeng Zhu
    • 1
  • Jilian Zhang
    • 1
  • Shichao Zhang
    • 1
    • 2
  1. 1.College of CS & ITGuangxi Normal UniversityGuilinChina
  2. 2.The Centre for QCIS, Faculty of Engineering and Information TechnologyUniversity of Technology SydneyAustralia

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