Saliency Estimation Using a Non-Parametric Low-Level Vision Model

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 centersurround inhibition windows have been adjusted by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc pa...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Naila Murray, Maria Vanrell, Xavier Otazu, C. Alejandro Parraga
• Keywords: Color Appearance, Computer Vision, CVPR 2011, Innate Spatial Pooling, Parameter