Salient Object Detection Using Window Mask Transferring with Multi-layer Background Contrast

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• 作者：Quan Zhou (17)
  Shu Cai (17)
  Shaojun Zhu (18)
  Baoyu Zheng (17)
  
  17. College of Telecommunication and Information Engineering ; Nanjing University of Posts and Telecommunications ; Nanjing ; People鈥檚 Republic of China
  18. Department of Computer and Information Science ; University of Pennsylvania ; Philadelphia ; PA ; USA
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9005
• 期：1
• 页码：221-235
• 全文大小：5,637 KB
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• 作者单位：Computer Vision -- ACCV 2014
• 丛书名：978-3-319-16810-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this paper, we present a novel framework to incorporate bottom-up features and top-down guidance to identify salient objects based on two ideas. The first one automatically encodes object location prior to predict visual saliency without the requirement of center-biased assumption, while the second one estimates image saliency using contrast with respect to background regions. The proposed framework consists of the following three basic steps: In the top-down process, we create a specific location saliency map (SLSM), which can be identified by a set of overlapping windows likely to cover salient objects. The binary segmentation masks of training windows are treated as high-level knowledge to be transferred to the test image windows, which may share visual similarity with training windows. In the bottom-up process, a multi-layer segmentation framework is employed, which is able to provide vast robust background candidate regions specified by SLSM. Then the background contrast saliency map (BCSM) is computed based on low-level image stimuli features. SLSM and BCSM are finally integrated to a pixel-accurate saliency map. Extensive experiments show that our approach achieves the state-of-the-art results over MSRA 1000 and SED datasets.