Saliency detection based on superpixels clustering and stereo disparity  

作　　者：GAO Shan-shan CHI Jing LI Li ZOU Ji-biao ZHANG Cai-ming 

机构地区：[1]School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China [2]School of Computer Science and Technology, Shandong University, Jinan 250100, China. [3]Shandong Provincial Key Laboratory of Digital Media Technology, Jinan 250014, China.

出　　处：《Applied Mathematics(A Journal of Chinese Universities)》2016年第1期68-80,共13页高校应用数学学报（英文版）（B辑）

基　　金：supported by NSFC Joint Fund with Guangdong under Key Project(U1201258);National Natural Science foundation of China(61402261;61303088;61572286);the scientific research foundation of Shandong Province of Outstanding Young Scientist Award(BS2013DX048);Shandong Ji’nan Science and Technology Development Project(201202015)

摘　　要：Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.

关 键 词：Saliency detection superpixels stereo disparity spatial coherence. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] O242.23[自动化与计算机技术—计算机科学与技术]