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	Automatic image annotation with long distance spatial-context
	Cao, Donglin ; Lin, Dazhen ; Yu, Jiansong ; Cao DL(曹冬林) ; Lin DZ(林达真)
	2014
关键词	Artificial intelligence Experiments Image analysis Image retrieval Image segmentation Random processes Semantics
英文摘要	Conference Name:2014 14th UK Workshop on Computational Intelligence, UKCI 2014. Conference Address: Bradford, West Yorkshire, United kingdom. Time:September 8, 2014 - September 10, 2014.; Because of high computational complexity, a long distance spatial-context based automatic image annotation is hard to achieve. Some state of art approaches in image processing, such as 2D-HMM, only considering short distance spatial-context (two neighbors) to reduce the computational complexity. However, these approaches cannot describe long distance semantic spatial-context in image. Therefore, in this paper, we propose a two-step Long Distance Spatial-context Model (LDSM) to solve that problem. First, because of high computational complexity in 2D spatial-context, we transform a 2D spatial-context into a 1D sequence-context. Second, we use conditional random fields to model the 1D sequence-context. Our experiments show that LDSM models the semantic relation between annotated object and background, and experiment results outperform the classical automatic image annotation approach (SVM).
语种	英语
出处	http://dx.doi.org/10.1109/UKCI.2014.6930181
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86909]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Cao, Donglin,Lin, Dazhen,Yu, Jiansong,et al. Automatic image annotation with long distance spatial-context. 2014-01-01.

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