Combining multi-layer integration algorithm with background prior and label propagation for saliency detection |
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| Affiliation: | 1. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China;2. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;1. Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, Guangdong, China;2. University of Virginia, Department of ECE, Charlottesville, VA 22904, USA;3. BJUT Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;1. The School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China;2. The School of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei Province 066004, China;1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China;2. Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK |
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| Abstract: | In this paper, we propose a novel approach to automatically detect salient regions in an image. Firstly, some corner superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the background labels based on ranking algorithm. Subsequently, we further employ an objectness measure to pick out and propagate foreground labels. Furthermore, an integration algorithm is devised to fuse both background-based saliency map and foreground-based saliency map, meanwhile an original energy function is acted as refinement before integration. Finally, results from multiscale saliency maps are integrated to further improve the detection performance. Our experimental results on five benchmark datasets demonstrate the effectiveness of the proposed method. Our method produces more accurate saliency maps with better precision-recall curve, higher F-measure and lower mean absolute error than other 13 state-of-the-arts approaches on ASD, SED, ECSSD, iCoSeg and PASCAL-S datasets. |
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| Keywords: | Corner Objectness Energy function Integration algorithm Multi-layer |
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