Folk art classification using support vector machine

Abstract

Tremendous amounts of effort have been carried out every year by the governments of all the countries to preserve art and culture. Art in the form of paintings, artifacts, music, dance, and cuisines of every country has the utmost importance. The study of Tribal arts provides deep insight into our history and acts as a milestone in the roadmap of our future. This paper focuses on three popular folk arts namely: Gond, Manjusha, and Warli. 300 images of each artwork have been collected from various online repositories. To generate a robust system, data augmentation is applied which results in 7510 images. A feature vector based on a generalized co-occurrence matrix, local binary pattern, HSV histogram, and canny edge detector is constructed and classification is performed using a linear support vector machine. 10- fold cross-validation produces 99.8% accuracy.