Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition
文献类型：期刊论文
| 作者 | Yuan, Chunfeng1 ; Li, Xi2; Hu, Weiming1; Ling, Haibin3; Maybank, Stephen J.4
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2014-02-01 |
| 卷号 | 23期号:2页码:658-672 |
| 关键词 | Covariance cuboid descriptor log-Euclidean Riemannian metric spatio-temporal directional pyramid co-occurrence matrix kernel machine action recognition |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | IMAGE FEATURES ; CLASSIFICATION ; CATEGORIES ; FLOW |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000329581800014 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3268]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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