Idol dataset
B. Sathya Bama, S. Mohamed Mansoor Roomi, D. Sabarinathan, M Senthilarasi, G. Manimala · 2021
Idols are rich descriptors capturing both visual and historical information about temples and therefore enhance the process of documenting and managing cultural heritage of a place. There are very limited annotated databases for artistic cultural heritage images especially for idols. To meet this need, we collected, annotated, and prepared a new database of Hindu Religious Idols. The first version of Idol dataset contains 14,592 images collected from Internet by querying three major search engines using 150 name manifestations related keywords about 31 idol categories. Correctly identifying a particular God/Goddess image in the form of paintings, photographs and sculptures is crucial. In this paper, we investigate the use of deep neural networks to solve the problem of recognizing religious idols. To achieve this result, the network is first pre-trained on 10 ImageNets and selected Densenet121 which outperforms the other networks. A modified Densenet architecture is proposed with a softmax output for idol recognition to achieve 74.9% and 84.28% of Top1 and Top 2 Rank Accuracies respectively on imbalanced learning and 74.93% and 84.02% Top1 and Top2 Rank accuracies respectively for weighted loss learning.