关键词: 计算机视觉, 显著目标检测, 边界先验, 颜色区别性, objectness

Abstract: Saliency detection is a fundamental part of computer vision applications, and the goal is to detect important pixels or regions in an image which attracts human visual attention most. By analyzing some recent methods, a new approach is proposed to solve detection errors problems and to enhance the adaptation of features in saliency detection. It detects saliency in the perspective of both object and background and integrates multi features. It extracts color distinctness feature in the perspective of object and extracts boundary prior feature in the perspective of background, and then combines the two features to obtain the corresponding map. In order to keep accuracy, it uses objectness feature to refine the saliency of detected regions. In comparison experiments, it achieves an average precision of 92.4% on MSRA-1000 databases, and achieves higher precision on CSSD dataset and ECSSD dataset. Experimental results demonstrate the used features make up for each other, which can enhance the saliency detection accuracy.

Key words: computer vision, saliency detection, boundary prior, color distinctness, objectness