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Machine learning approaches for visual information retrieval

  • Frédéric Precioso
  • Matthieu Cord
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

In this chapter, we first describe the main stages for deriving image representation from visual local descriptors which has been described in Chapter 2. Coding and pooling steps are detailed. We then remind briefly some of the most usual (dis-)similarity measures between histograms, paying a particular attention to a class of similarity functions, called kernels, we deeply investigate. We present several strategies to build similarity measures. These similarities can then either represent the basis of a similarity search system or be integrated into more powerful machine learning frameworks to address classification, retrieval or detection tasks.

Keywords

Support Vector Machine Unlabeled Data Machine Learning Approach Multiple Kernel Learn Stochastic Gradient Descent 
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

© The Author(s) 2012

Authors and Affiliations

  1. 1.UNS I3S UMR 7271/UNS_CNRSSophia Antipolis CedexFrance
  2. 2.UPMC LIP6 UMR 7606ParisFrance

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