A fast fixed-point algorithm for independent component analysis
Neural Computation
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimality of Spatial Attention for Object Detection
Attention in Cognitive Systems
On the role of context in probabilistic models of visual saliency
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An eye fixation database for saliency detection in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Computational versus Psychophysical Bottom-Up Image Saliency: A Comparative Evaluation Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tag-Saliency: Combining bottom-up and top-down information for saliency detection
Computer Vision and Image Understanding
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This paper presents a novel approach to visual saliency that relies on a contextually adapted representation produced through adaptive whitening of color and scale features. Unlike previous models, the proposal is grounded on the specific adaptation of the basis of low level features to the statistical structure of the image. Adaptation is achieved through decorrelation and contrast normalization in several steps in a hierarchical approach, in compliance with coarse features described in biological visual systems. Saliency is simply computed as the square of the vector norm in the resulting representation. The performance of the model is compared with several state-of-the-art approaches, in predicting human fixations using three different eye-tracking datasets. Referring this measure to the performance of human priority maps, the model proves to be the only one able to keep the same behavior through different datasets, showing free of biases. Moreover, it is able to predict a wide set of relevant psychophysical observations, to our knowledge, not reproduced together by any other model before.