Perception based representations for computational colour
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Global salient information maximization for saliency detection
Image Communication
A saliency detection model based on local and global kernel density estimation
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Bio-inspired visual saliency detection and its application on image retargeting
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Visual saliency detection with center shift
Neurocomputing
Static saliency vs. dynamic saliency: a comparative study
Proceedings of the 21st ACM international conference on Multimedia
Letters: Background contrast based salient region detection
Neurocomputing
Tag-Saliency: Combining bottom-up and top-down information for saliency detection
Computer Vision and Image Understanding
Saliency detection based on integrated features
Neurocomputing
Color boosted visual saliency detection and its application to image classification
Multimedia Tools and Applications
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Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.