SASITHRADEVI ANBALAGAN
ABSTRACT
Kolam, the art of floor drawing is a long standing and time-honoured practice in India. The traditional art has its own impact in Indian culture because of its role in religious practice. The culture of kolam has decreased over time due to modernization. The photograph of these artistic images is the fundamental component that can provide the visual and sensory experiences to future generations. So, we need a sincere attempt to preserve and document the culture of floor art images. Image classification aids in easing the process of preservation and documentation of kolam images. As a primary step, we aim to classify the kolam images viz, footprint, swastik, geometric, plant and animal motifs in this article. Inspired by the performance of deep networks in most of the vision-based problems, we developed a new kolamNet to classify the different motifs of kolam. KolamNet is attained by incorporating attention mechanism into EfficientNet. Attention mechanism assures lightweight functionality and refines the deep feature along with end to end training. To validate KolamNet, Kolam dataset is created to accomplish the classification task. Extensive experimentation is conducted on Kolam dataset to prove the effectiveness of the KolamNet to classify the motifs. KolamNet shows better capability in terms of performance metrics – accuracy-97%, precision-0.96, recall-0.97 and F1score-0.97 when compared to related state of the art deep network architectures.
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Index Terms
- KolamNet: An attention based model for kolam classification✱
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