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Context-Based Support Vector Machines for Interconnected Image Annotation

  • Hichem Sahbi
  • Xi Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6492)

Abstract

We introduce in this paper a novel image annotation approach based on support vector machines (SVMs) and a new class of kernels referred to as context-dependent. The method goes beyond the naive use of the intrinsic low level features (such as color, texture, shape, etc.) and context-free kernels, in order to design a kernel function applicable to interconnected databases such as social networks. The main contribution of our method includes (i) a variational approach which helps designing this function using both intrinsic features and the underlying contextual information resulting from different links and (ii) the proof of convergence of our kernel to a positive definite fixed-point, usable for SVM training and other kernel methods. When plugged in SVMs, our context-dependent kernel consistently improves the performance of image annotation, compared to context-free kernels, on hundreds of thousands of Flickr images.

Keywords

Support Vector Machine Visual Feature Reproduce Kernel Hilbert Space Image Annotation Equal Error Rate 
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

  • Hichem Sahbi
    • 1
  • Xi Li
    • 1
    • 2
    • 3
  1. 1.CNRS Telecom ParisTechParisFrance
  2. 2.School of Computer ScienceThe University of AdelaideAustralia
  3. 3.NLPR, CASIABeijingChina

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