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  Published Paper Details:

  Paper Title

DEEP LEARNING FOR ART CHARACTERIZATION

  Authors

  Niraj Sonje,  Dr. Rupesh C. Jaiswal,  Dr. Girish P. Potdar,  Manish R. Khodaskar

  Keywords

CNN, Data Augmentation, Transfer Learning, Art

  Abstract


Art in the form of paintings have been fascinating humans since ages. With the digitization of art museums, it becomes imperative to devise tools that can automatically classify artists based on paintings to help visitors, buyers and museum curators gain more information effortlessly. Image classification models, in general, aim at extracting distinct information from images and assign them a particular category. In this paper we use transfer learning to fine tune the existing CNN models such as ResNet, that has been pre-trained on ImageNet, and apply multiple different data augmentation techniques to tackle the challenge of classifying 50 artists. We intend to observe and analyse the hyper-parameters and augmentation technique giving the best results. We also make some attempts of inventing new architecture of neural network dedicate to painting understanding and new approach for data augmentation.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2211081

  Paper ID - 227223

  Page Number(s) - a687-a694

  Pubished in - Volume 10 | Issue 11 | November 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Niraj Sonje,  Dr. Rupesh C. Jaiswal,  Dr. Girish P. Potdar,  Manish R. Khodaskar,   "DEEP LEARNING FOR ART CHARACTERIZATION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 11, pp.a687-a694, November 2022, Available at :http://www.ijcrt.org/papers/IJCRT2211081.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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