Exploring ensemble learning for classifying geometric patterns: insights from quaternion cartesian fractional Hahn moments

Abstract

The classification of geometric patterns, particularly in Islamic art, presents a compelling challenge for the field of computer vision due to its intricate symmetry and scale invariance. This study proposes an ensemble learning framework to classify geometric patterns, leveraging the novel quaternion cartesian fractional Hahn moments (QCFrHMs) as a robust feature extraction method. QCFrHMs integrate the fractional Hahn polynomial and quaternion algebra to provide compact, invariant descriptors for geometric patterns. Combined with Zernike Moments, this dual-feature approach ensures resilience against rotation, scaling, and noise variations. The extracted features were evaluated using support vector machines (SVM), random forest, and a soft-voting ensemble classifier. Experiments were conducted on a dataset comprising 1,204 geometric images categorized into two symmetry groups (p4m and p6m). Results demonstrated that the ensemble classifier outperformed standalone models, achieving a classification accuracy of 82.15%. The integration of QCFrHMs significantly enhanced the system's robustness compared to traditional Zernike-only approaches, which aligns with findings in prior studies. This research contributes to the fields of image processing and pattern recognition by introducing an efficient feature extraction technique combined with ensemble learning for precise and scalable geometric pattern classification. The implications extend to art preservation, architectural analysis, and automated indexing of cultural heritage imagery.