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Descriptor Learning Using Convex Optimisation

  • Karen Simonyan
  • Andrea Vedaldi
  • Andrew Zisserman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal: first, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity; second, it is shown that dimensionality reduction can also be formulated as a convex optimisation problem, using the nuclear norm to reduce dimensionality. Both of these problems use large margin discriminative learning methods. The third contribution is a new method of obtaining the positive and negative training data in a weakly supervised manner. And, finally, we employ a state-of-the-art stochastic optimizer that is efficient and well matched to the non-smooth cost functions proposed here. It is demonstrated that the new learning methods improve over the state of the art in descriptor learning for large scale matching, Brown et al. [2], and large scale object retrieval, Philbin et al. [10].

Keywords

Image Retrieval Convex Optimisation Convex Optimisation Problem Pooling Region Feature Pair 
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 2012

Authors and Affiliations

  • Karen Simonyan
    • 1
  • Andrea Vedaldi
    • 1
  • Andrew Zisserman
    • 1
  1. 1.Visual Geometry GroupUniversity of OxfordUK

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