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231224s2014 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2013.2291319
|2 doi
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|a pubmed24n0839.xml
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|a DE-627
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|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Yuan, Chunfeng
|e verfasserin
|4 aut
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| 245 |
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|a Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition
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|c 2014
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 22.10.2015
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| 500 |
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|a Date Revised 14.08.2015
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Li, Xi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Weiming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ling, Haibin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Maybank, Stephen J
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 23(2014), 2 vom: 07. Feb., Seite 658-72
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|x 1941-0042
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| 773 |
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|g volume:23
|g year:2014
|g number:2
|g day:07
|g month:02
|g pages:658-72
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2013.2291319
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