Elements of information theory
Elements of information theory
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Saliency, Scale and Image Description
International Journal of Computer Vision
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biologically Inspired Saliency Map Model for Bottom-up Visual Attention
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Boosting Saliency in Color Image Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Distinctiveness, Detectability, and Robustness of Local Image Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Piecewise Image Registration in the Presence of Multiple Large Motions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Kernel-based Recognition of Human Actions Using Spatiotemporal Salient Points
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Two Bayesian methods for junction classification
IEEE Transactions on Image Processing
Region and constellations based categorization of images with unsupervised graph learning
Image and Vision Computing
Constellations and the unsupervised learning of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Entropy versus heterogeneity for graphs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Characterizing graphs using approximate von Neumann entropy
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Graph clustering using the Jensen-Shannon Kernel
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Graph characterizations from von Neumann entropy
Pattern Recognition Letters
Graph Kernels from the Jensen-Shannon Divergence
Journal of Mathematical Imaging and Vision
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The scale saliency feature extraction algorithm by Kadir and Brady has been widely used in many computer vision applications. However, when compared to other feature extractors, its computational cost is high. In this paper, we analyze how saliency evolves through scale space, demonstrating an intuitive idea: if an image region is homogeneous at higher scales, it will probably also be homogeneous at lower scales. From the results of this analysis we propose a Bayesian filter based on Information Theory, that given some statistical knowledge about the images being considered, discards pixels from an image before applying the scale saliency detector. Experiments show that if our filter is used, the efficiency of the original algorithm increases with low localization and detection error.