Dynamic texture recognition using 3D random features |
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Author: | Zhao, Xiaochao1; Lin, Yaping1; Liu, Li2,3 |
Organizations: |
1Hunan Provincial Key Lab of Trusted System and Network, Hunan University, China 2Center for Machine Vision and Signal Analysis, University of Oulu, Finland 3College of System Engineering, National University of Defense Technology, China |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.1 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202003249087 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2019
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Publish Date: | 2020-03-24 |
Description: |
AbstractIn this paper, we present a novel, simple but effective approach for dynamic texture recognition using 3D random features. Compared with the existing dynamic texture recognition approaches using carefully designed features for high performance, our method use only a few 3D random filters to extract spatio-temporal features from local dynamic texture blocks, which are further encoded into a low-dimensional feature vector. To explore the representative power of the 3D random features, we use two different encoding schemes, the learning-based Fisher vector encoding and the learning-free binary encoding. The proposed method is tested on the UCLA and DynTex databases with various evaluation protocols. Experimental results demonstrate the high performance of our method for dynamic texture recognition. see all
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Series: |
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing |
ISSN: | 1520-6149 |
ISSN-E: | 2379-190X |
ISSN-L: | 1520-6149 |
ISBN: | 978-1-4799-8131-1 |
ISBN Print: | 978-1-4799-8132-8 |
Pages: | 2102 - 2106 |
Article number: | 8683054 |
DOI: | 10.1109/ICASSP.2019.8683054 |
OADOI: | https://oadoi.org/10.1109/ICASSP.2019.8683054 |
Host publication: |
44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings |
Conference: |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
113 Computer and information sciences |
Subjects: | |
Copyright information: |
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