Unified Saliency Detection Model Using Color and Texture Features

Libo Zhang, Lin Yang and Tiejian Luo

PLOS ONE, 2016, vol. 11, issue 2, 1-14

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0149328

DOI: 10.1371/journal.pone.0149328

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