Co-saliency Detection for RGBD Images Based on Multi-constraint Superpixels Matching and Co-cellular Automata
Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images.It is a newly emerging topic in computer vision community.Different from the existing co-saliency methods focusing on RGB images,this paper proposes a novel cosaliency detection model for RGBD images,which utilizes the depth information to enhance identification of co-saliency.First,we utilize the existing single saliency maps as the initialization,then we use multiple cues to compute combination inter-images similarity to match inter-neighbors for each superpixel.Especially,we extract high dimensional features for each image region with a deep convolutional neural network as semantic cue.Finally,we introduce a modified 2-layer Co-cellular Automata to exploit depth information and the intrinsic relevance of similar regions through interactions with neighbors in multi-scene.The experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed framework.
RGBD Co-saliency Cellular automata Semantic feature Multi-constraint
Zhengyi Liu Feng Xie
Key Laboratory of Intelligent Computing and Signal Processing,Ministry of Education,Anhui University Co-Innovation Center for Information Supply and Assurance Technology,Hefei,China
国际会议
广州
英文
132-143
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）