Unsupervised detection of image object with any class

	Unsupervised detection of image object with any class
	Song X.-R.; Wu Z.-Y.
	2014
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	ISBN/1004924X
卷号	22 期号:1 页码:160-168
摘要	To measure a variety of objects of an image and to reduce the detection time, an unsupervised object detection model was established to provide location priors. The model was mainly based on three image cues of a object, and they are saliency detection, color contrast and superpixel straddling. To determine the likelihood of image object contained in a window, the saliency scores of the three cues were calculated, and the saliency cues of the three objects were fused in a simple Bayesian framework by a machine learning center-surrounding proportion parameter. In experiments on the challenging PASCAL VOC 07 dataset, it shows that the detection rate is 28.94%, the hit rate is 96.99% and the combined measuring result is better than any cue alone. In experiments on MSRC dataset, it shows that the proposed model is generic and efficient, the detection rate is 80.64%, the hit rate is 99.10% and the average processing time is 40% less than that of Bogdan's model. These results from extensive field tests suggest that proposed model can provide better location priors to the object recognition and image segmentation where the location of object is unknown.
收录类别	EI
语种	中文
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/44500
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Song X.-R.,Wu Z.-Y.. Unsupervised detection of image object with any class[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2014,22(1):160-168.
APA	Song X.-R.,&Wu Z.-Y..(2014).Unsupervised detection of image object with any class.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,22(1),160-168.
MLA	Song X.-R.,et al."Unsupervised detection of image object with any class".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 22.1(2014):160-168.

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