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	Multimodal Mixed Conditional Random Field Model for Category-Independent Object Detection
	Jian-Hua Zhang; Jian-Wei Zhang; Sheng-Yong Chen; and Ying Hu
	2012
会议名称	IEEE First International Conference on Cognitive Systems and Information Processing
会议地点	中国
英文摘要	Category-independent object detection is extremely useful for many robot vision tasks. Most existing methods rank a lot of regions by measuring their object-likeness. However, to obtain a sufficient object covering rate too many regions need to be sampled. In this paper, we present a novel method that directly detects and localizes category-independent objects. We develop a novel model which is named as “mixed robust higher-order conditional random field” model which combines 2D and 3D data into a uniform framework. A set of novel features is developed based on 2D and 3D saliency and oversegments. The potentials used in this model are computed from these features. Extensive experiments are carried out on a public RGB-D dataset. By comparison with state-ofthe- art ranking methods, the experimental results show the comparable performance of category-independent object detection without sampling a large number of extra regions.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/3855]
专题	深圳先进技术研究院_集成所
作者单位	2012
推荐引用方式 GB/T 7714	Jian-Hua Zhang,Jian-Wei Zhang,Sheng-Yong Chen,et al. Multimodal Mixed Conditional Random Field Model for Category-Independent Object Detection[C]. 见:IEEE First International Conference on Cognitive Systems and Information Processing. 中国.

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