电子学报2017，Vol.45Issue(11)：2593-2601,9.DOI:10.3969/j.issn.0372-2112.2017.11.004
基于全卷积网络的语义显著性区域检测方法研究
The Semantic Salient Region Detection Algorithm Based on the Fully Convolutional Networks
摘要
Abstract
The existing salient region detection algorithms based on visual stimulus and prior knowledge are difficult to detect some complicated salient regions.The human vision system can distinguish these complicated salient regions because of the rich semantic knowledge in the human visual system.We construct a semantic salient region detection network using the fully convolutional structure.Learning the mapping from the low-level features to the human semantic cognition,our network can extract semantic salient region effectively.Aiming to the defects of the semantic salient region map,we introduce the color information,object boundary information and spatial consistency information to derive accurate superpixellevel foreground and background probability.At last,we fuse the foreground and background probability,semantic information and spatial consistency information to derive the final salient region map.The experiments comparing with the state-of-the-art 15 algorithms on 6 data sets demonstrate the effectiveness of our algorithm.关键词
语义信息/全卷积网络/颜色外观模型/显著性区域检测Key words
semantic information/fully convolutional network/color appearance model/salient region detection分类
信息技术与安全科学引用本文复制引用
郑云飞,张雄伟,曹铁勇,孙蒙..基于全卷积网络的语义显著性区域检测方法研究[J].电子学报,2017,45(11):2593-2601,9.基金项目
国家自然科学基金(No.61471394) （No.61471394）
国家青年自然科学基金(No.61402519) （No.61402519）
江苏省自然科学基金(No.BK2012510,No.BK20140071,No.BK20140074) （No.BK2012510,No.BK20140071,No.BK20140074）
