Multimodal mixed conditional random field model for category-independent object detection

	Multimodal mixed conditional random field model for category-independent object detection
	Jian-Hua Zhang; Jian-Wei Zhang; Sheng-Yong Chen; Ying Hu
刊名	Advances in Intelligent Systems and Computing
	2014
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-of-the-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	SCI
原文出处	http://link.springer.com/chapter/10.1007/978-3-642-37835-5_54
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/5434]
专题	深圳先进技术研究院_集成所
作者单位	Advances in Intelligent Systems and Computing
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[J]. Advances in Intelligent Systems and Computing,2014.
APA	Jian-Hua Zhang,Jian-Wei Zhang,Sheng-Yong Chen,&Ying Hu.(2014).Multimodal mixed conditional random field model for category-independent object detection.Advances in Intelligent Systems and Computing.
MLA	Jian-Hua Zhang,et al."Multimodal mixed conditional random field model for category-independent object detection".Advances in Intelligent Systems and Computing (2014).