Classificação de padrões através de wavelets e métodos bayesianos

• Ano de defesa: 2011
• Autor(a) principal: Foster, Douglas Camargo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Ciência da Computação; UFSM; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Classificação de padrões; Métodos bayesianos; Wavelets; Imagens; Pattern classification; Bayesian methods; Images; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/5374

The interest in the pattern classification field has increased due to challenging applications and also due to computational demands, specially when big datasets have to be analyzed. Statistical classification methods, as the based Bayes rules decision theory, apply the parameter estimation from a training dataset for recognizing different classes inside the dataset. In this work it is investigated the contribution of using the discrete wavelet transformation (DWT) for feature extraction during the classification process. From the scale coefficients of different decomposition levels, new training datasets, which are used in Bayesian classifier, are formed. For the one and two dimensional transforms the Daubechies wavelet family is considered. Three specifically wavelet functions are analyzed (Haar, Daubechies Db2 and Db8). Also, a hybrid methodology is proposed, in which 2D and 1D wavelet transformations are applied in consecutive stages of data analysis. For the evaluation of the one dimensional transform methodology, two different unidimensional datasets are used: one is composed by synthetic data, and the other is composed by network traffic data (DARPA1999 dataset). For the evaluation of 2D and hybrid methodologies two-dimensional data are considered. The two-dimensional data are images with different digital pictures with and without using ash light. One advantage of applying the hybrid methodology is the maintenance of the classification regularity and the increase of correct classification in some cases.