Madhubani Art Classification using transfer learning with deep feature fusion and decision fusion based techniques

Some traditional Indian art forms enjoy widespread popularity across the world. One of the most prominent among these is the Madhubani style. This art form’s rich heritage and beauty enthrall the connoisseurs and continue to inspire new designs catering to the changing tastes of prevalent fashion. Preservation of these traditional art forms is the need of the hour. Modern technological advances can be utilized with great advantage for this purpose. Since a database of Madhubani art forms was hitherto unavailable, an attempt is made in this work to create one from scratch. Five different classes of Madhubani art, i.e., Bharni, Godna, Kachni, Kohbar, and Tantrik, are identified, and the collected images are annotated with these classes. Classification of the art images is attempted using the handcrafted Local Binary Pattern (LBP) texture descriptors and state-of-the-art Convolutional Neural Networks (CNNs). The Transfer Learning approach with CNNs is employed to classify the designs. An attempt is made to obtain a better classification accuracy than the one provided by standard CNNs. Towards this end, the current work proposes a fusion of features extracted from several deep CNNs, decision fusion-based classification based on averaging prediction score (FAVG), and maximum vote score (FMAX). The proposed method’s performance is tested on our Madhubani art dataset and compared against several standard pre-trained CNNs available in the literature. The proposed approaches provide significantly higher classification accuracy for Madhubani art patterns, with decision fusion based on averaging prediction score (FAVG) approach being the best. The maximum accuracy, specificity, and error rate scores are 98.82%, 99.72%, and 1.18%, respectively. This is the first such attempt, and the excellent results motivate further work to develop content-based image retrieval tools and evolutionary design-based tools for automating the development of new designs. These endeavors are expected to go a long way in preserving precious art heritage and fostering its rapid growth in the world market. The dataset will be made publically available for further experimentation.