# An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms
## 2 Literature Survey  
The diversity in Indian classical dance forms comes from the diversity in Indian traditions and culture. Moreover, these dance forms are often symbolized for expressing human emotions. Thus, to preserve the Indian culture of fine arts, dance form recognition becomes an important problem statement and the subtle differences found in these dance forms make this task even more challenging. The authors in [1] explore the use of classical Indian dance to teach geometry and other mathematical concepts. The given methodology of categorical content analysis suggests a framework for Asian Indian students to learn mathematical shapes through Bharatanatyam. The study also acknowledges the benefits of classical dance in learning basic geometric patterns. Extensive research work has been published by authors in [2], wherein they established chronological relation between kathak footwork and geometry. Many such publications indicate the high correlation between ICD and mathematics, especially geometry. In the paper [3], researchers propose a novel framework to classify Indian classical dance forms from videos. The representations are extracted through deep convolutional neural network (DCNN) [4] and optical flow. Moreover, these representations are trained on a multi-class linear support vector machine (SVM [5]. Nityantar, a pose oblivious Indian classical dance sequence classification system [6], shows the working of deep descriptors with handcrafted pose signature on the ICD dataset. The authors in [7] present a novel classification solution by giving a new action descriptor. The proposed descriptor not only classifies ICD but also classifies common human actions. The research article by authors in [8] focuses on feature extraction and then uses AdaBoost multi-class classifier [9] on multi-feature fusion. The authors in [10] give another pioneer contribution specific to recognizing classical dance forms and mudras using an imagery dataset of hand mudras from different classical dance forms. The support vector machine (SVM) classifier is given histogram of oriented (HOG) features as input for classification. Additionally, the use of transfer learning with different CNN architectures is optimal for solving various classification problem statements. The application compares different architectures, namely AlexNet, GoogLeNet, ResNet-18, VGG-16, and VGG-19 for transfer learning with pre-trained CNNs applied to breast cancer detection on infrared images given by authors in [11], cancer detection from a small dataset. An objective quality metric to evaluate the perceived visual quality of 3D meshes is proposed. The method relies on a pre-trained convolutional neural network, i.e., VGG, to extract features from the distorted mesh. Image classification also involves class prediction based on object detection. It needs object localization for which various models have been proposed and implemented [12; 13; 14]. Object detection includes instance segmentation as well [15]. The state-of-the-art approaches for object detection have been implemented for various use cases which include both image and video dataset [16]. This paper provides an in-detail description of feature extraction techniques on a dataset with pre-processed dance form images using different CNN architectures, followed by the results and evaluation section, which compares the performance of these architectures with the proposed approach. Another paper [17] proposes a deep learning three-step-pipeline for Indian dance style classification. The first step is to extract essential joint locations of the dancer from each video frame, which helps identify the region's location. For the last step, high-level recurrent neural networks are used. The whole pipeline provides a way to use multiple representations from a single video. [18] used histogram of oriented optical flow (HOOF) features with sparse representations. Firstly, they represent each frame of a dance video by a pose descriptor based on a histogram of oriented optical flow (HOOF) hierarchically.