A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shunting inhibition does not have a divisive effect on firing rates
Neural Computation
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Transform Coding of Images
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
A Framework for Low Level Feature Extraction
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Focus-of-Attention from Local Color Symmetries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated Learning of Saliency, Complex Features, and Object Detectors from Cluttered Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Learning of Relational Object Class Models
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Natural Image Statistics and Low-Complexity Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable discriminant feature selection for image retrieval and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
On the modeling of DCT and subband image data for compression
IEEE Transactions on Image Processing
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A learned saliency predictor for dynamic natural scenes
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
A novel hierarchical model of attention: maximizing information acquisition
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Two-layer average-to-peak ratio based saliency detection
Image Communication
Bayesian modeling of visual attention
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Multiscale discriminant saliency for visual attention
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
Abrupt motion tracking using a visual saliency embedded particle filter
Pattern Recognition
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A decision-theoretic formulation of visual saliency, first proposed for top-down processing (object recognition) (Gao & Vasconcelos, 2005a), is extended to the problem of bottom-up saliency. Under this formulation, optimality is defined in the minimum probability of error sense, under a constraint of computational parsimony. The saliency of the visual features at a given location of the visual field is defined as the power of those features to discriminate between the stimulus at the location and a null hypothesis. For bottom-up saliency, this is the set of visual features that surround the location under consideration. Discrimination is defined in an information-theoretic sense and the optimal saliency detector derived for a class of stimuli that complies with known statistical properties of natural images. It is shown that under the assumption that saliency is driven by linear filtering, the optimal detector consists of what is usually referred to as the standard architecture of V1: a cascade of linear filtering, divisive normalization, rectification, and spatial pooling. The optimal detector is also shown to replicate the fundamental properties of the psychophysics of saliency: stimulus pop-out, saliency asymmetries for stimulus presence versus absence, disregard of feature conjunctions, and Weber's law. Finally, it is shown that the optimal saliency architecture can be applied to the solution of generic inference problems. In particular, for the class of stimuli studied, it performs the three fundamental operations of statistical inference: assessment of probabilities, implementation of Bayes decision rule, and feature selection.