Discrete Orthogonal Moment Descriptors Applied In Image Classification And Object Recognition Enhanced By Machine Learning Methods

Abstract

Orthogonal moments play a crucial role in image analysis, including applications in image reconstruction and the extraction of consistent, robust features, making them essential for modern computational imaging. This study examines the effectiveness and performance evaluation of orthogonal: Tchebichef moments, Krawtchouk moments, Charlier moments, and Hahn moments for image reconstruction, feature description, and their application in image classification and object recognition using advanced machine learning techniques. We present the construction process of orthogonal image moments. Then the deriving of invariant moments based on Tchebichef, Krawtchouk, Charlier, and Hahn polynomials. These moments are then employed to generate feature vectors specifically tailored for image classification and object recognition tasks. To evaluate feature robustness, we conduct extensive classification experiments on two distinct image databases using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) classifiers. The results demonstrate that invariant moments are highly effective at capturing discriminative image features and maintain reliable performance under various noise conditions, including salt-and-pepper noise, underscoring their applicability in real-world scenarios.