Image saliency detection using Gabor texture cues

• Published in: Multimedia tools and applications Vol. 75; no. 24; pp. 16943 - 16958
• Main Authors: Chen, Zhi-hua, Liu, Yi, Sheng, Bin, Liang, Jian-ning, Zhang, Jing, Yuan, Yu-bo
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2016; Springer Nature B.V
• Subjects: Algorithms; Color; Computer Communication Networks; Computer Science; Cues; Data Structures and Information Theory; Fourier transforms; Image detection; Image retrieval; Methods; Multimedia Information Systems; Object recognition; Semantics; Special Purpose and Application-Based Systems; Surface layer; Texture; Superpixel; Image segmentation; Texture features; Saliency detection
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-015-2965-y

Image saliency analysis plays an important role in various applications such as object detection, image compression, and image retrieval. Traditional methods for saliency detection ignore texture cues. In this paper, we propose a novel method that combines color and texture cues to robustly detect image saliency. Superpixel segmentation and the mean-shift algorithm are adopted to segment an original image into small regions. Then, based on the responses of a Gabor filter, color and texture features are extracted to produce color and texture sub-saliency maps. Finally, the color and texture sub-saliency maps are combined in a nonlinear manner to obtain the final saliency map for detecting salient objects in the image. Experimental results show that the proposed method outperforms other state-of-the-art algorithms for images with complex textures.