Efficient semantic image segmentation with multi-class ranking prior

	Efficient semantic image segmentation with multi-class ranking prior
	Pei, Deli; Li, Zhenguo; Ji, Rongrong; Sun, Fuchun
发表期刊	COMPUTER VISION AND IMAGE UNDERSTANDING
	2014-03-01
卷号	120 页码:81-90
文章类型	Article
摘要	Semantic image segmentation is of fundamental importance in a wide variety of computer vision tasks, such as scene understanding, robot navigation and image retrieval, which aims to simultaneously decompose an image into semantically consistent regions. Most of existing works addressed it as structured prediction problem by combining contextual information with low-level cues based on conditional random fields (CRFs), which are often learned by heuristic search based on maximum likelihood estimation. In this paper, we use maximum margin based structural support vector machine (S-SVM) model to combine multiple levels of cues to attenuate the ambiguity of appearance similarity and propose a novel multi-class ranking based global constraint to confine the object classes to be considered when labeling regions within an image. Compared with existing global cues, our method is more balanced between expressive power for heterogeneous regions and the efficiency of searching exponential space of possible label combinations. We then introduce inter-class co-occurrence statistics as pairwise constraints and combine them with the prediction from local and global cues based on S-SVMs framework. This enables the joint inference of labeling within an image for better consistency. We evaluate our algorithm on two challenging datasets which are widely used for semantic segmentation evaluation: MSRC-21 dataset and Stanford Background dataset and experimental results show that we obtain high competitive performance compared with state-of-the-art methods, despite that our model is much simpler and efficient. (C) 2013 Elsevier Inc. All rights reserved.
关键词	Computer Vision Machine Learning Semantic Segmentation Structural Svms
WOS标题词	Science & Technology ; Technology
关键词[WOS]	SHIFT
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000331924500007
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/40878
专题	复杂系统认知与决策实验室_听觉模型与认知计算
推荐引用方式 GB/T 7714	Pei, Deli,Li, Zhenguo,Ji, Rongrong,et al. Efficient semantic image segmentation with multi-class ranking prior[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2014,120:81-90.
APA	Pei, Deli,Li, Zhenguo,Ji, Rongrong,&Sun, Fuchun.(2014).Efficient semantic image segmentation with multi-class ranking prior.COMPUTER VISION AND IMAGE UNDERSTANDING,120,81-90.
MLA	Pei, Deli,et al."Efficient semantic image segmentation with multi-class ranking prior".COMPUTER VISION AND IMAGE UNDERSTANDING 120(2014):81-90.