Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 视频内容安全

	Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition
	Yuan, Chunfeng1 ; Li, Xi 2; Hu, Weiming1 ; Ling, Haibin 3; Maybank, Stephen J.4
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2014-02-01
卷号	23 期号:2 页码:658-672
文章类型	Article
摘要	In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved.
关键词	Covariance Cuboid Descriptor Log-euclidean Riemannian Metric Spatio-temporal Directional Pyramid Co-occurrence Matrix Kernel Machine Action Recognition
WOS标题词	Science & Technology ; Technology
关键词[WOS]	IMAGE FEATURES ; CLASSIFICATION ; CATEGORIES ; FLOW
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:000329581800014
引用统计	被引频次：13[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/173211/3268
专题	多模态人工智能系统全国重点实验室_视频内容安全
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Adelaide, Sch Comp Sci, Adelaide, SA 5005, Australia 3.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA 4.Univ London Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Yuan, Chunfeng,Li, Xi,Hu, Weiming,et al. Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(2):658-672.
APA	Yuan, Chunfeng,Li, Xi,Hu, Weiming,Ling, Haibin,&Maybank, Stephen J..(2014).Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(2),658-672.
MLA	Yuan, Chunfeng,et al."Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.2(2014):658-672.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
06665089_TIP.pdf（3487KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载