Texture Classification based on Spectral Analysis and Haralick Features
Classificação de Texturas mediante análise espectral e Parâmetros de Haralick
Resumo
Abstract
In this work we discuss a method to classify a set of texturized images based on the extraction of their Haralick Features. This kind of Classification is capable of providing texture-based measurements (such as contrast or correlation) and use them as main parameters to classify the same type of patterns in other images. In order to improve the classification success ratio a spectral analysis of the textures and, therefore, the use of filters, before the classification step, is proposed here. In this work the classification success has been evaluated using Mean and Canny filters. On the other hand, the Principal Component Analysis is used to optimize the features extracted for the patterns on each image, before introduced into the classifier. With this method the classifying success ratio for the KTH-TIPS subset and 10,000 different permutations was increased --in average-- from 72.28\% to 84.25\%.
Keywords: Haralick Features, Texture Classification, Image Processing, Spectrum Analysis, Principal Component Analysis.
Neste trabalho é discutido um método de classificação de Texturas baseado na extração de parâmetros de Haralick. Este tipo de classificação é capaz de fornecer medidas de texturas (como por exemplo contraste ou correlação) em padrões presentes em imagens e usá -las para classificar o mesmo tipo de padrões em outras imagens. Com o objetivo de melhorar a taxa de sucesso na classificação foi proposto realizar uma análise espectral e, por tanto, o uso de filtros em passo prévio ao processo de classificação. Este trabalho apresenta uma avaliação do desempenho da classificação por meio do uso dos filtros Média e Canny. Por outro lado, a análise de Componentes Principais foi usada para otimizar os parâmetros extraídos dos padrões nas imagens, antes destas serem introduzidas no classificador. Com este método o sucesso na taxa de classificação para a biblioteca de imagens KTH-TIPS e um conjunto de 10.000 permutações diferentes foi incrementado –em média– de 72.28% para 84.25%.
Palavras chave: Parâmetros de Haralick, Classificação de Texturas, Processamento de Imagens, Análise espectral, PCA
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