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Multi-label Classification for Image Annotation via Sparse Similarity Voting

  • Tomoya Sakai
  • Hayato Itoh
  • Atsushi Imiya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6469)

Abstract

We present a supervised multi-label classification method for automatic image annotation. Our method estimates the annotation labels for a test image by accumulating similarities between the test image and labeled training images. The similarities are measured on the basis of sparse representation of the test image by the training images, which avoids similarity votes for irrelevant classes. Besides, our sparse representation-based multi-label classification can estimate a suitable combination of labels even if the combination is unlearned. Experimental results using the PASCAL dataset suggest effectiveness for image annotation compared to the existing SVM-based multi-labeling methods. Nonlinear mapping of the image representation using the kernel trick is also shown to enhance the annotation performance.

Keywords

Test Image Training Image Sparse Representation Image Annotation Orthogonal Match Pursuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tomoya Sakai
    • 1
  • Hayato Itoh
    • 2
  • Atsushi Imiya
    • 3
  1. 1.Faculty of EngineeringNagasaki UniversityJapan
  2. 2.Graduate School of Science and TechnologyChiba UniversityJapan
  3. 3.Institute of Media and Information TechnologyChiba UniversityJapan

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