Texture Feature Extraction and Classification Using Machine Learning Techniques

Texture, a crucial aspect of an image, is something made up of components that are related to one another. Reliable feature extraction in image files requires the use of a texture-based categorization method, which is significant. This study proposes an effective method for classifying textures using machine learning (ML) approaches. Using these ML classifiers, which are in the form of artificial intelligence (AI), programmers can predict results exactly without providing instructed to do so explicitly. The proposed study focuses on the creation of own dataset in the form of CSV file, to do so Haralick features (contrast, dissimilarity homogeneity, energy, and correlation) extracted from the Brodatz texture dataset. Different ML algorithms are used like: K-Nearest Neighbor, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, and AdaBoost Classifier which are experimented on the created dataset to classify the texture of Brodatz dataset. Proposed approach exhibits better results with 100% accuracy with less computation time as compared to previous work in the literature.