Abstract
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.
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Larin, A., Seredin, O., Kopylov, A., Kuo, SY., Huang, SC., Chen, BH. (2014). Parametric Representation of Objects in Color Space Using One-Class Classifiers. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_23
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DOI: https://doi.org/10.1007/978-3-319-08979-9_23
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