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Comparison of shallow and deep learning models for classification of Lasem batik patterns

Batik is a method of cloth decoration by dyeing process which is practiced particularly in Java, Indonesia. The batik pattern varies from one region to another. In this paper, we explore and compare the shallow and deep learning model to classify Lasem batik motifs automatically. Several shallow models are employed, such as the support vector machines, Gaussian naïve Bayes, and decision tree with the gray level co-occurrence matrix as the features. For the deep learning models, we use the convolutional neural network and deep belief network. We collect 698 batik images which can be classified into seven different types of Lasem batik motif. From the experiments, we found that the shallow model, particularly the support vector machines with linear function kernel performs best, even compared to the deep learning models.