University of Oulu

X. Zhao, Y. Lin and L. Liu, "Dynamic Texture Recognition Using 3D Random Features," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 2102-2106. doi: 10.1109/ICASSP.2019.8683054

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:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-03-24


In 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.

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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
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
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