Abstract
In this paper we propose a Bayesian filter for the Kadir Scale Saliency Detector. Such filter is addressed to deal with the main bottleneck of the Kadir detector, which is the scale space search for all pixels in the image. Given some statistical knowledge about images considered, we show that it is possible to discard some points before applying the Kadir detector by using Information Theory and Bayesian Analysis, increasing efficiency with low error. Our method is based on the intuitive idea that homogeneous (not salient) image regions at high scales probably will be also homogeneous at lower scales of scale space.
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Suau, P., Escolano, F. (2007). Exploiting Information Theory for Filtering the Kadir Scale-Saliency Detector. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_19
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DOI: https://doi.org/10.1007/978-3-540-72849-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72848-1
Online ISBN: 978-3-540-72849-8
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