CCCV 2015: Computer Vision pp 407-417 | Cite as

Superpixel-Based Global Contrast Driven Saliency Detection in Low Contrast Images

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 546)

Abstract

Due to the low signal to noise ratio, saliency detection in low contrast images has been a great challenge in computer vision. In this paper we propose a novel approach to detect salient object based on the computation of global saliencies in superpixel image blocks. This method tackles the image through a simple contrast measure, which first computes the global difference of two superpixels to obtain the resulting saliency map. Then, the map is refined by introducing the inter-superpixel similarity approach. The proposed model perfectly extracts the salient object in low contrast visibility conditions, which has been tested on three public datasets, as well as a nighttime image dataset. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art saliency detection models.

Keywords

Superpixel segmentation Low contrast Salient object detection Global saliency 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial SystemWuhan University of Science and TechnologyWuhanChina

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