Search

There is a greater need to develop and establish Artificial Intelligence (AI) and its subdomains, as the computing requirements are increasingly met by the emerging hardware technologies. The machine-learning techniques are well suited for the learning-based AI applications that are useful to our daily life. Further, the machine-learning applications can resolve numerous problems of the South Indian Classical Dance (SICD). Nevertheless, these aspects are not yet addressed thoroughly owing to the vastness of the domain. Moreover, the lack of a combined expertise in both domains of the SICD and the computing aggravates the problem. The automatic identification and annotation of a vital aspect called sthanas (foot postures) are necessary for the process of digitizing, archiving and analytics of the SICD. Hence in this paper, we propose a framework to classify the SICD images based on the foot sthanas. The proposed framework incorporates methods to convert raw data to a curated dataset, and extract principal features that are unique to the various foot posture in classical dance, in order to generate an accurate classification. Among the different techniques that were used to evaluate the accuracy, Naive Bayes, trained with the domain-specific features, outperformed all other classification models. The methodology followed in this work can be applied to various national and international dance forms with proper incorporation of their domain-specific features. [ABSTRACT FROM AUTHOR]