Title :
Bernoulli mixture models for binary images
Author :
Juan, Alfons ; Vidal, Enrique
Author_Institution :
Dept. de Sistemas Inf. y Comput., Univ. Politecnica de Valencia, Spain
Abstract :
Mixture modelling is a hot area in pattern recognition. Although most research in this area has focused on mixtures for continuous data, there are many pattern recognition tasks for which binary or discrete mixtures are better suited. This paper focuses on the use of Bernoulli mixtures for binary data and, in particular, for binary images. Results are reported on a task of handwritten Indian digits.
Keywords :
handwritten character recognition; image classification; image representation; Bernoulli mixture models; binary data; binary images; discrete mixtures; handwritten Indian digits; image classification; image representation; pattern recognition; Books; Handwriting recognition; Maximum likelihood estimation; Optical character recognition software; Pattern classification; Pattern recognition; Pixel; Testing; Text categorization; Training data;
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
Print_ISBN :
0-7695-2128-2
DOI :
10.1109/ICPR.2004.1334543