CASIA OpenIR  > 模式识别国家重点实验室  > 视频内容安全
Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition
Yuan, Chunfeng1; Li, Xi2; Hu, Weiming1; Ling, Haibin3; Maybank, Stephen J.4
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2014-02-01
Volume23Issue:2Pages:658-672
SubtypeArticle
AbstractIn 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.
KeywordCovariance Cuboid Descriptor Log-euclidean Riemannian Metric Spatio-temporal Directional Pyramid Co-occurrence Matrix Kernel Machine Action Recognition
WOS HeadingsScience & Technology ; Technology
WOS KeywordIMAGE FEATURES ; CLASSIFICATION ; CATEGORIES ; FLOW
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000329581800014
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3268
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
06665089_TIP.pdf(3487KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yuan, Chunfeng]'s Articles
[Li, Xi]'s Articles
[Hu, Weiming]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yuan, Chunfeng]'s Articles
[Li, Xi]'s Articles
[Hu, Weiming]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yuan, Chunfeng]'s Articles
[Li, Xi]'s Articles
[Hu, Weiming]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 06665089_TIP.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.