Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems

18A Texture Classification System Based on an Adaptive Histogram Equalized Shearlet Transform

K. Gopalakrishnan¹*, V. Karthikeyan^1† and P.T. Vanathi²

¹Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India

²PSG College of Technology, Coimbatore, Tamilnadu, India

Abstract

Texture-based image classification cation is a crucial part in many intelligence tasks, including object recognition, clinical and forensic analysis, content retrieval, document subdivision, surface inspection, etc. Synthesizing a huge digital image from a small digital sample (i.e., structural content) of an image is known as texture image synthesis. Texture analysis can be a challenging duty due to the variability and difficulty of textures. Addressing these challenges requires careful consideration of the features used for analysis, the tools and algorithms used, and the quality of the image data being analyzed. Texture image categorization can be done in two different ways: in the Transform and the spatial domain. Images are broken into their constituent sub-bands and translated to the frequency in the transform domain. Depending on the width of the filter, the data for a given sub-band will be a different set of frequencies. Each sub-band is used to determine the mean, standard deviation, and other statistical properties. Co-occurrence features are also collected from the co-occurrence matrix for each sub-band in addition to the statistical features mentioned above. Minimum distance classifiers ...