Panchadeva: Sculpture Image Classification using CNN-SVM - Akshath Rao; Sindhu C; Abrar Suhail; Aayush Mehta; Saksham Dube (Journal of Population Therapeutics and Clinical Pharmacology, 2023)
Rich culture and heritage have been a very promising factor when it has come to the land of India. India is a land which is enriched with a vast amount of architectural sculpture, rare sculptures, temples and many more. When it comes to the classification as well as recognition of these Rare Indian Sculptures, it is often a very challenging task. In the realm of image recognition, classification, and identification, entity recognition of Indian sculptures might be seen as one of the most complex and challenging challenges. This project primarily consists of a database which is manually constructed. The database consists for a total of five entities belonging to the various Indian Gods. In this project, we have considered Lord Ganesha, Lord Hanuman, Lord Krishna, Lord Shiva and lastly Lord Vishnu thus adding up to a total of five entity classes. Every image has a distinctive feature which separates and differentiates it from the rest of the images or pictures. The orientation, angles, sizes as well as the colours of all these images play a very crucial and important role in the training and processing of the machine learning model which is to be implemented. Here, the model is trained on a total of 500 images with each of 100 images belonging to one entity class. Support Vector Machine and Convolutional Neural Network, respectively, can be used to assess the efficacy and accuracy of the proposed model. Using a combination of the Deep Learning, Convolutional Neural Network (CNN) along with Sequential model with categorical cross-entropy and SoftMax functions assisted by SVM(Support Vector Machine) -the model that this project trained has enabled us to achieve an accuracy of 94%.