Isocentric color saliency in images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
REM: relational entropy-based measure of saliency
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Salient region detection using discriminative feature selection
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Image information in digital photography
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Selective spatio-temporal interest points
Computer Vision and Image Understanding
Modulating Shape Features by Color Attention for Object Recognition
International Journal of Computer Vision
Non-local spatial redundancy reduction for bottom-up saliency estimation
Journal of Visual Communication and Image Representation
Visual saliency detection with center shift
Neurocomputing
Bayesian modeling of visual attention
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Saliency maps of high dynamic range images
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Top-Down saliency by multi-scale contextual pooling
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Saliency detection using joint spatial-color constraint and multi-scale segmentation
Journal of Visual Communication and Image Representation
Saliency detection based on integrated features
Neurocomputing
Top-Down Saliency Detection via Contextual Pooling
Journal of Signal Processing Systems
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A discriminant formulation of top-down visual saliency, intrinsically connected to the recognition problem, is proposed. The new formulation is shown to be closely related to a number of classical principles for the organization of perceptual systems, including infomax, inference by detection of suspicious coincidences, classification with minimal uncertainty, and classification with minimum probability of error. The implementation of these principles with computational parsimony, by exploitation of the statistics of natural images, is investigated. It is shown that Barlow's principle of inference by the detection of suspicious coincidences enables computationally efficient saliency measures which are nearly optimal for classification. This principle is adopted for the solution of the two fundamental problems in discriminant saliency, feature selection and saliency detection. The resulting saliency detector is shown to have a number of interesting properties, and act effectively as a focus of attention mechanism for the selection of interest points according to their relevance for visual recognition. Experimental evidence shows that the selected points have good performance with respect to 1) the ability to localize objects embedded in significant amounts of clutter, 2) the ability to capture information relevant for image classification, and 3) the richness of the set of visual attributes that can be considered salient.