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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2781299
|2 doi
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|a pubmed24n0933.xml
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|a (DE-627)NLM28007977X
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|a (NLM)29346101
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|a DE-627
|b ger
|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 Jia, Chengcheng
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Stacked Denoising Tensor Auto-Encoder for Action Recognition With Spatiotemporal Corruptions
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| 264 |
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|c 2018
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 30.07.2018
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| 500 |
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|a Date Revised 30.07.2018
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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 Spatially or temporally corrupted action videos are impractical for recognition via vision or learning models. It usually happens when streaming data are captured from unintended moving cameras, which bring occlusion or camera vibration and accordingly result in arbitrary loss of spatiotemporal information. In reality, it is intractable to deal with both spatial and temporal corruptions at the same time. In this paper, we propose a coupled stacked denoising tensor auto-encoder (CSDTAE) model, which approaches this corruption problem in a divide-and-conquer fashion by jointing both the spatial and temporal schemes together. In particular, each scheme is a SDTAE designed to handle either spatial or temporal corruption, respectively. SDTAE is composed of several blocks, each of which is a denoising tensor auto-encoder (DTAE). Therefore, CSDTAE is designed based on several DTAE building blocks to solve the spatiotemporal corruption problem simultaneously. In one DTAE, the video features are represented as a high-order tensor to preserve the spatiotemporal structure of data, where the temporal and spatial information are processed separately in different hidden layers via tensor unfolding. In summary, DTAE explores the spatial and temporal structure of the tensor representation, and SDTAE handles different corrupted ratios progressively to extract more discriminative features. CSDTAE couples the temporal and spatial corruptions of the same data through a thorough step-by-step procedure based on canonical correlation analysis, which integrates the two sub-problems into one problem. The key point is solving the spatiotemporal corruption in one model by considering them as noises in either spatial or temporal direction. Extensive experiments on three action data sets demonstrate the effectiveness of our model, especially when large volumes of corruption in the video
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Shao, Ming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Sheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Handong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fu, Yun
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 4 vom: 20. Apr., Seite 1878-1887
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:27
|g year:2018
|g number:4
|g day:20
|g month:04
|g pages:1878-1887
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TIP.2017.2781299
|3 Volltext
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|d 27
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|e 4
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|h 1878-1887
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