Saliency Detection via Manifold Ranking Based on Robust Foreground
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ncy Detection via Manifold Ranking Based on Robust Foreground Wei-Ping Ma Wen-Xin Li Jin-Chuan Sun Peng-Xia Cao Lanzhou Institute of Physics, China Academy of Space Technology, Lanzhou 730000, China
Abstract: The graph-based manifold ranking saliency detection only relies on the boundary background to extract foreground seeds, resulting in a poor saliency detection result, so a method that obtains robust foreground for manifold ranking is proposed in this paper. First, boundary connectivity is used to select the boundary background for manifold ranking to get a preliminary saliency map, and a foreground region is acquired by a binary segmentation of the map. Second, the feature points of the original image and the filtered image are obtained by using color boosting Harris corners to generate two different convex hulls. Calculating the intersection of these two convex hulls, a final convex hull is found. Finally, the foreground region and the final convex hull are combined to extract robust foreground seeds for manifold ranking and getting final saliency map. Experimental results on two public image datasets show that the proposed method gains improved performance compared with some other classic methods in three evaluation indicators: precision-recall curve, F-measure and mean absolute error. Keywords: Saliency detection, manifold ranking, boundary connectivity, convex hull, robust foreground.
1 Introduction Saliency detection technology is an intelligent information processing technology in the pre-processing stage of computer vision, mainly researching how to let the computer simulate the human visual attention mechanism in the unknown scene to quickly and efficiently capture the most important and informative object. At present, much research has been conducted on the calculation of object saliency, and many algorithm models have been proposed and widely applied to numerous fields of computer vision, such as image segmentation[1, 2], object recognition[3] and image compression[4]. According to different information processing perspectives, saliency detection can be divided into two approaches[5]: top-down (task-driven) methods and bottom-up (data-driven) methods. The top-down method[6, 7] needs to integrate specific prior knowledge, high-level semantic information and other human perception to complete saliency detection, which is not universal. The bottom-up method[8−10] pays more attention to features such as contrast, color, and texture. It only needs to use the underlying information of each image to quickly and easily detect the salient object. Meanwhile, it can improve the detection accuracy by combining the prior knowledge such as center, background and convex hull. In this paper, we adopt the bottom-up method to complete the salient object detec Research Article Manuscript received April 8, 2020; accepted July 7, 2020 Recommended by Associate Editor Zhi-Jie Xu © Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany
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