title: Unified Saliency Detection Model Using Color and Texture Features.
abstract: Saliency detection attracted attention of many researchers and had become a very active area of research. Recently, many saliency detection models have been proposed and achieved excellent performance in various fields. However, most of these models only consider low-level features. This paper proposes a novel saliency detection model using both color and texture features and incorporating higher-level priors. The SLIC superpixel algorithm is applied to form an over-segmentation of the image. Color saliency map and texture saliency map are calculated based on the region contrast method and adaptive weight. Higher-level priors including location prior and color prior are incorporated into the model to achieve a better performance and full resolution saliency map is obtained by using the up-sampling method. Experimental results on three datasets demonstrate that the proposed saliency detection model outperforms the state-of-the-art models.
authors.affiliation: School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Libo
authors.initials: L
authors.lastname: Zhang
authors.affiliation: School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
authors.affiliation: School of Computer and Control, University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Tiejian
authors.initials: T
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical