Texture Classification Using Texture-based Feature Extraction Algorithms

Texture is one of the significant characteristics used in identifying objects of interest or regions in an image. Texture is an important characteristic of surface property in visual scenes and is a power cue in visual perception. The real applications of texture classification are remote sensing, medical imaging, industrial inspection and pattern recognition. Texture images are highly affected by rotation and illumination. Extracting texture features that are rotation-invariant and insensitive to illumination with high classification accuracy is still a challenge. Texture analysis has been a popular area of study in computer vision for decades. In this thesis, six texture-based feature extractors that may perform variously to rotation and illumination are used namely Local Binary Patterns (LBP), Complete Local Binary Patterns (CLBP), Segmentation-based Fractal Texture Analysis (SFTA), Histogram of Oriented Gradients (HOG), Rotation Invariant Histogram of Oriented Gradients (RIHOG) and Haralick feature extractor. They are implemented and tested on three benchmark texture databases, such as The Columbia-Utrecht Database (CUReT), University of Oulu Texture database (OUTex) and Textured Surfaces Database. For feature matching, two classifiers are used namely Naive Bayes and Support Vector Machines (SVM). A comparative study is presented at the end of the experimental evaluations on texture classification.