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An Experimental Comparison of Three Guiding Principles for the Detection of Salient Image Locations: Stability, Complexity, and Discrimination

  • Dashan Gao
  • Nuno Vasconcelos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)

Abstract

We present an experimental comparison of the performance of representative saliency detectors from three guiding principles for the detection of salient image locations: locations of maximum stability with respect to image transformations, locations of greatest image complexity, and most discriminant locations. It is shown that discriminant saliency performs better in terms of 1) capturing relevant information for classification, 2) being more robust to image clutter, and 3) exhibiting greater stability to image transformations associated with variations of 3D object pose. We then investigate the dependence of discriminant saliency on the underlying set of candidate discriminant features, by comparing the performance achieved with three popular feature sets: the discrete cosine transform, a Gabor, and a Haar wavelet decomposition. It is show that, even though different feature sets produce equivalent results, there may be advantages in considering features explicitly learned from examples of the image classes of interest.

Keywords

Discrete Cosine Transform Image Class Saliency Detector Image Transformation Interest Point Detector 
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 2007

Authors and Affiliations

  • Dashan Gao
    • 1
  • Nuno Vasconcelos
    • 1
  1. 1.Statistical Visual Computing Laboratory, Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093USA

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