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NovemE - Color Space Net for Image Classification

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Intelligent Information and Database Systems (ACIIDS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12672))

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Abstract

Image classification is one of the basic applications of computer vision where several deep convolutional neural networks (DCNN) have been able to achieve state of the art results. In this paper, we explore the efficacious effect of color space transformations on image classification. After empirically establishing that color space transforms indeed affects the accuracy of the classification in DCNN, we propose NovemE, an ensemble-based model made up of nine (novem) base learners. We use transfer learning with significantly reduced training time and improved accuracy of classification. This model integrates different color spaces and DCNNs in order to achieve a higher accuracy in classifying the given data. We experimented with CINIC10 and Street View House Number (SVHN) datasets and were successful in achieving significant improvement on the current state of the art results on these datasets.

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Authors and Affiliations

  1. Dhirubhai Ambani Institute of Information and Communication Technology (DAIICT), Gandhinagar, Gujarat, India

    Urvi Oza, Sarangi Patel & Pankaj Kumar

Authors
  1. Urvi Oza
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  2. Sarangi Patel
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  3. Pankaj Kumar
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Corresponding author

Correspondence to Urvi Oza .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

  3. Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Oza, U., Patel, S., Kumar, P. (2021). NovemE - Color Space Net for Image Classification. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_42

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  • DOI: https://doi.org/10.1007/978-3-030-73280-6_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73279-0

  • Online ISBN: 978-3-030-73280-6

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