Salient region detection from natural image statistics

The selection of salient regions in an image is the first step in many computer vision algorithms, e.g. objects recognition, classification or tracking. Our hypothesis is that local image statistics are indicative for saliency. We combine natural image statistics with the detection of salient regions. Particularly, we consider the integrated Weibull distribution as a parameterized model, which provides a good fit to the statistics of natural images. Here we show how distinct regimes of the integrated Weibull distribution leads to various local saliency mechanisms. With model selection techniques from information theory, we can determine the probability for every distinct regime, to explain the statistical properties of local image content. Hence, resulting in new algorithms for salient region detection.

[1]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[2]  David R. Anderson,et al.  Multimodel Inference , 2004 .

[3]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[4]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[5]  G..  A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .

[6]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[7]  Luc Van Gool,et al.  Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions , 2000, BMVC.

[8]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[9]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Nicole Vincent,et al.  Use of power law models in detecting region of interest , 2007, Pattern Recognit..

[11]  KochChristof,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 1998 .

[12]  Cor J. Veenman,et al.  Robust Scene Categorization by Learning Image Statistics in Context , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[13]  David Mumford,et al.  Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[14]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[15]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.

[16]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[17]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[18]  Michael A. Stephens,et al.  Goodness of fit for the extreme value distribution , 1977 .

[19]  Luc Van Gool,et al.  Content-Based Image Retrieval Based on Local Affinely Invariant Regions , 1999, VISUAL.

[20]  J. V. van Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[21]  Arnold W. M. Smeulders,et al.  c ○ 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Six-Stimulus Theory for Stochastic Texture , 2002 .

[22]  Arnold W. M. Smeulders,et al.  Fragmentation in the vision of scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[23]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[24]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[25]  Eero P. Simoncelli.  Modeling the joint statistics of images in the wavelet domain , 1999, Optics & Photonics.