Texture Classification based on Spectral Analysis and Haralick Features

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 averagefrom 72.28% to 84.25%.

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

“…Texture features are visual patterns that show homogeneity in the image. The texture feature has essential information regarding the pattern of image structure and its relationship to the environment around the image [27]. Some techniques that can be used to extract texture features are Local Binary Pattern (LBP), Gray-level Co-occurrence Matrix (GLCM), and Haralick features [28] [29] [30].…”