Saliency Detection Based on Multi-scale Image Features
Chaoqun Zheng, Xiaozhi Zheng, Guizhong Wang, Shuo Tian, Qiang Guo · 2016
In this paper, a simple method is proposed for detecting salient regions by utilizing muti-scale features at a superpixel-based level. To guarantee the accuracy of the detected region, we make use of superpixel to form a new measurement, instead of using the level of pixel. Majority of saliency models consider visual features such as orientation, color, intensity and the importance of visual features has not yet been fully explored. Here, we take texture feature into consideration in particular, using the gray level co-occurrence matrix(GLCM) as a texture descriptor. Specifically, a new local smoothing filter based on GLCM is proposed to smooth image and remove some simple texture before getting GLCM. Experimental results demonstrate that our approach significantly outperforms some existing popular methods with full resolution and well-defined boundary.