Parametric Representation of Objects in Color Space Using One-Class Classifiers

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• 作者：Aleksandr Larin (20)
  Oleg Seredin (21)
  Andrey Kopylov (21)
  Sy-Yen Kuo (22)
  Shih-Chia Huang (23)
  Bo-Hao Chen (23)
  
• 关键词：One ; class classification ; Pixel classifiers ; Fire detection ; Flame detection ; Support Vector Data Description ; PCA
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8556
• 期：1
• 页码：300-314
• 全文大小：1,143 KB
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  14. Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers. In: Proc. of the 19th International Joint Conference on Artificial Intelligence, pp. 702-07 (2005)
  
• 作者单位：Aleksandr Larin (20)
  Oleg Seredin (21)
  Andrey Kopylov (21)
  Sy-Yen Kuo (22)
  Shih-Chia Huang (23)
  Bo-Hao Chen (23)
  
  20. Moscow Institute of Physics and Technology, Moscow, Russia
  21. Tula State University, Tula, Russia
  22. National Taiwan University, Taipei, Taiwan
  23. National Taipei University of Technology, Taipei, Taiwan
  
• ISSN：1611-3349

文摘

Two new approaches to parametrization of specific (flame representative) part of a color space, labeled by an expert, are presented. The first concept is to apply D.?Tax’s one-class classifier as a steerable descriptor of such a complex volumetric structure. The second concept is based on approximation of the training data by a set of elliptic cylinders arranged along the principal components. Parameters of such elliptic cylinders describe the training set. The efficiency of the approaches has been proven by experimental study which let allowed us to compare the standard Gaussian Mixture Model based approach with the two proposed in the paper.