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An Affine Invariant Salient Region Detector

  • Timor Kadir
  • Andrew Zisserman
  • Michael Brady
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)

Abstract

In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to affine invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.

The detector has significantly different properties to operators based on kernel convolution, and we examine three aspects of its behaviour: invariance to viewpoint change; insensitivity to image perturbations; and repeatability under intra-class variation. Previous work has, on the whole, concentrated on viewpoint invariance. A second contribution of this paper is to propose a performance test for evaluating the two other aspects.

We compare the performance of the saliency detector to other standard detectors including an affine invariance interest point detector. It is demonstrated that the saliency detector has comparable viewpoint invariance performance, but superior insensitivity to perturbations and intra-class variation performance for images of certain object classes.

Keywords

Object Class Region Detector Saliency Detector Sampling Window Background Clutter 
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 2004

Authors and Affiliations

  • Timor Kadir
    • 1
  • Andrew Zisserman
    • 1
  • Michael Brady
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK

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