Co-Bootstrapping Saliency

Huchuan Lu, Xiaoning Zhang, Jinqing Qi, Na Tong, Xiang Ruan, Ming-Hsuan Yang
2017 IEEE Transactions on Image Processing  
In this paper, we propose a visual saliency detection algorithm to explore the fusion of various saliency models in a manner of bootstrap learning. First, an original bootstrapping model, which combines both weak and strong saliency models, is constructed. In this model, image priors are exploited to generate an original weak saliency model, which provides training samples for a strong model. Then, a strong classifier is learned based on the samples extracted from the weak model. We use this
more » ... ssifier to classify all the salient and non-salient superpixels in an input image. To further improve the detection performance, multi-scale saliency maps of weak and strong model are integrated, respectively. The final result is the combination of the weak and strong saliency maps. The original model indicates that the overall performance of the proposed algorithm is largely affected by the quality of weak saliency model. Therefore, we propose a co-bootstrapping mechanism, which integrates the advantages of different saliency methods to construct the weak saliency model thus addresses the problem and achieves a better performance. Extensive experiments on benchmark data sets demonstrate that the proposed algorithm outperforms the stateof-the-art methods. Index Terms- Saliency detection, weak saliency model, strong saliency model, co-bootstrapping. Recently, many salient object detection methods have been proposed which can be categorized as bottom-up stimulidriven [1]-[30] and top-down task-driven [31]-[39] methods. Bottom-up methods are usually based on low-level visual Manuscript Huchuan Lu (SM'12) received the M.Sc. degree in signal and information processing and the Ph.D. degree in system engineering from the Dalian University of Technology (DUT), Dalian, China, in 1998 and 2008, respectively. He joined DUT in 1998 as a Faculty Member, where he is currently a Full Professor with the School of Information and Communication Engineering. His current research interests include computer vision and pattern recognition with a focus on visual tracking, saliency detection, and segmentation. He is a member of the Association for Computing Machinery and an Associate Editor of the IEEE TRANSACTIONS ON CYBERNETICS. Xiaoning Zhang received the B.E. degree in electronic information engineering from the Dalian University of Technology (DUT), Dalian, China, in 2015. She is currently pursuing the master's degree with the School of Information and Communication Engineering, DUT. Her research interest is in saliency detection. Jinqing Qi (M'14) received the Ph.D. degree in communication and integrated system from the Tokyo Institute of Technology, Tokyo, Japan, in 2004. He is currently an Associate Professor of Information and Communication Engineering with the Dalian University of Technology, Dalian, China. His recent research interests focus on computer vision, pattern recognition, and machine learning. Na Tong received the B.E. degree in electronic information engineering and the M.S. degree in signal and information processing from the Dalian University of Technology (DUT), Dalian, China, in 2012 and 2015, respectively. Her research interest is in saliency detection.
doi:10.1109/tip.2016.2627804 pmid:28113932 fatcat:p62itxqkxvefrlaijgk3nyfrdi