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An Innovative Approach for Automatic Genre-Based Fine Art Painting Classification

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Advanced Computational and Communication Paradigms

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 706))

Abstract

Recent advances in digital image processing, computer vision, etc., have led to many approaches to the classification of fine art painting. The focus was made to develop an automatic painting classification system based on their genre. The expanding database of digital painting images makes it imperative to develop an automated method to annotate paintings with metadata as painter, genre, painting tool used, style, etc., so that problems like image retrieval, searching, organizing, and artistic recommendations become convenient and efficient. The aim was to classify painting database into five genres. The database consisted of 1229 digital painting images. The method adopted for the task was feature extraction from the images, using each feature individually for classification and then combining the features based on weighted majority voting to form an ensemble classifier. It was observed that Local Binary Pattern (LBP) was the best performing feature. The ensembled model achieved an accuracy of 80.41%. We have included analysis of our work and have discussed the performance of the various features deployed for the painting classification task.

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Author information

Authors and Affiliations

  1. SRM University, Chennai, 603203, India

    Alexis Paul & C. Malathy

Authors
  1. Alexis Paul
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  2. C. Malathy
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Corresponding author

Correspondence to Alexis Paul .

Editor information

Editors and Affiliations

  1. Department of Computer Application, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

  3. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majitar, Rangpo, Sikkim, India

    Debanjan Konar

  4. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majitar, Rangpo, Sikkim, India

    Udit Kr. Chakraborty

  5. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majitar, Rangpo, Sikkim, India

    Chingtham Tejbanta Singh

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Paul, A., Malathy, C. (2018). An Innovative Approach for Automatic Genre-Based Fine Art Painting Classification. In: Bhattacharyya, S., Chaki, N., Konar, D., Chakraborty, U., Singh, C. (eds) Advanced Computational and Communication Paradigms. Advances in Intelligent Systems and Computing, vol 706. Springer, Singapore. https://doi.org/10.1007/978-981-10-8237-5_3

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  • DOI: https://doi.org/10.1007/978-981-10-8237-5_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8236-8

  • Online ISBN: 978-981-10-8237-5

  • eBook Packages: EngineeringEngineering (R0)

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