An Empirical Study on Feature Extraction for the Classification of Textural and Natural Images
Syed Azar Ali, Chih‐Cheng Hung · 2016
Most image classification algorithms consist of feature extraction and image classification. Hence, feature extraction is a critical step for obtaining an accurate classification result. In this study, we perform the classification experiments based on the image features extracted using Local Binary Pattern operator (LBP), Discrete Wavelet Transforms (DWT) and Color Features (CF) for image classification. A comparison is made using the different combination of features; 1) LBP features only, 2) LBP and DWT features, and 3) LBP, DWT and CF features with a slight modification. Our goal is to determine what types of features are useful for improving the classification results. The Linear Support Vector Machine (SVM) algorithm is used to classify each pixel into a class based on the features used to obtain a classified image. Preliminary experimental results show that the hybrid of DWT, LBP, and CF gives the highest accuracy.