Deep discriminative model for video classification |
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Author: | Tavakolian, Mohammad1; Hadid, Abdenour1 |
Organizations: |
1Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 1.2 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2020041415344 |
Language: | English |
Published: |
Springer Nature,
2018
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Publish Date: | 2020-04-14 |
Description: |
AbstractThis paper presents a new deep learning approach for video-based scene classification. We design a Heterogeneous Deep Discriminative Model (HDDM) whose parameters are initialized by performing an unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBM). In order to avoid the redundancy of adjacent frames, we extract spatiotemporal variation patterns within frames and represent them sparsely using Sparse Cubic Symmetrical Pattern (SCSP). Then, a pre-initialized HDDM is separately trained using the videos of each class to learn class-specific models. According to the minimum reconstruction error from the learnt class-specific models, a weighted voting strategy is employed for the classification. The performance of the proposed method is extensively evaluated on two action recognition datasets; UCF101 and Hollywood II, and three dynamic texture and dynamic scene datasets; DynTex, YUPENN, and Maryland. The experimental results and comparisons against state-of-the-art methods demonstrate that the proposed method consistently achieves superior performance on all datasets. see all
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Series: |
Lecture notes in computer science |
ISSN: | 0302-9743 |
ISSN-E: | 1611-3349 |
ISSN-L: | 0302-9743 |
ISBN: | 978-3-030-01225-0 |
ISBN Print: | 978-3-030-01224-3 |
Pages: | 401 - 418 |
DOI: | 10.1007/978-3-030-01225-0_24 |
OADOI: | https://oadoi.org/10.1007/978-3-030-01225-0_24 |
Host publication: |
Computer Vision – ECCV 2018. ECCV 2018 |
Host publication editor: |
Ferrari, V. Sminchisescu, C. Weiss, Y. Hebert, M. |
Conference: |
European Conference on Computer Vision |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
213 Electronic, automation and communications engineering, electronics |
Subjects: | |
Funding: |
The financial support of the Academy of Finland and Infotech Oulu is acknowledged. |
Copyright information: |
© Springer Nature Switzerland AG 2018. This is a post-peer-review, pre-copyedit version of an article published in Computer Vision – ECCV 2018. ECCV 2018. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01225-0_24. |