Performance Comparison of Learned Features from Autoencoder and Shape-Based Hu Moments for Batik Classification

Batik classification depends critically on effective feature extraction to capture the unique geometric and visual characteristics of batik patterns. This study compares two distinct feature extraction methods for batik classification: learned features extracted via a convolutional autoencoder, and shape-based handcrafted features derived from Hu Moments. While autoencoders automatically learn complex latent representations that adapt to intricate pattern variations, Hu Moments provide invariant shape descriptors robust to rotation, scaling, and translation. The methodology involves extracting Hu Moment features and autoencoder latent features from the same batik image dataset, followed by evaluation with identical classifiers to ensure a fair comparison. Experimental results reveal key trade-offs: Hu Moments offer robustness and interpretability in capturing shape geometry, whereas autoencoder features better model complex, non-linear patterns. These findings highlight the complementary strengths of classical and learned feature extraction techniques, offering valuable insights for optimizing batik classification. This research advances feature extraction methodologies in cultural heritage image analysis, with broader applicability to pattern-rich domains like batik classification.