Spatiotemporal Derivative Pattern: A Dynamic Texture Descriptor for Video Matching

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• 作者：Farshid Hajati (17)
  Mohammad Tavakolian (17)
  Soheila Gheisari (17) (18)
  Ajmal Saeed Mian (19)
  
  17. Electrical Engineering Department ; Tafresh University ; Tafresh ; Iran
  18. Electrical Engineering Department ; Central Tehran Branch ; Islamic Azad University ; Tehran ; Iran
  19. Computer Science and Software Engineering ; The University of Western Australia ; Crawley ; WA ; 6009 ; Australia
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9007
• 期：1
• 页码：626-641
• 全文大小：5,240 KB
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• 作者单位：Computer Vision -- ACCV 2014
• 丛书名：978-3-319-16813-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

We present Spatiotemporal Derivative Pattern (SDP), a descriptor for dynamic textures. Using local continuous circular and spiral neighborhoods within video segments, SDP encodes the derivatives of the directional spatiotemporal patterns into a binary code. The main strength of SDP is that it uses fewer frames per segment to extract more distinctive features for efficient representation and accurate classification of the dynamic textures. The proposed SDP is tested on the Honda/UCSD and the YouTube face databases for video based face recognition and on the Dynamic Texture database for dynamic texture classification. Comparisons with existing state-of-the-art methods show that the proposed SDP achieves the overall best performance on all three databases. To the best of our knowledge, our algorithm achieves the highest results reported to date on the challenging YouTube face database.