@inproceedings{2b911e6688b0417da04d2c57df0afbbc,
title = "Parametric representation of objects in color space using one-class classifiers",
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.",
keywords = "Fire detection, Flame detection, One-class classification, PCA, Pixel classifiers, Support Vector Data Description",
author = "Aleksandr Larin and Oleg Seredin and Andrey Kopylov and Kuo, \{Sy Yen\} and Huang, \{Shih Chia\} and Chen, \{Bo Hao\}",
year = "2014",
doi = "10.1007/978-3-319-08979-9\_23",
language = "英语",
isbn = "9783319089782",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "300--314",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 10th International Conference, MLDM 2014, Proceedings",
address = "德国",
note = "10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014 ; Conference date: 21-07-2014 Through 24-07-2014",
}