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Learning Based Image Classification Techniques

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Computational Intelligence in Communications and Business Analytics (CICBA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1579))

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Abstract

Although, deep learning approaches have attained remarkable advancements in the application of image classification, they require a large amount of training samples and machines with high computing power. Collecting huge samples against each class for training is a difficult task, sometimes even not possible. To tackle these disadvantages of deep learning-based approach, new paradigms of machine learning, such as Few-Shot Learning (FSL), One-Shot-Learning (OSL), and Zero-Shot-Learning (ZSL) have been developed. The paper presents a survey on various image classification methods which have been developed based on the FSL, OSL, or ZSL paradigm. This paper also highlights a comparative study of the methods and a summary of the methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, North Eastern Regional Institute of Science and Technology, Arunachal Pradesh, India

    Nayan Kumar Sarkar & Moirangthem Marjit Singh

  2. Department of Computer Science, Vidyasagar University, West Bengal, India

    Utpal Nandi

Authors
  1. Nayan Kumar Sarkar
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  2. Moirangthem Marjit Singh
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  3. Utpal Nandi
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Corresponding author

Correspondence to Nayan Kumar Sarkar .

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Editors and Affiliations

  1. Assam University, Silchar, India

    Somnath Mukhopadhyay

  2. Assam University, Silchar, India

    Sunita Sarkar

  3. Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  4. University of Kalyani, Nadia, India

    Jyotsna Kumar Mandal

  5. Assam University, Silchar, India

    Sudipta Roy

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Sarkar, N.K., Singh, M.M., Nandi, U. (2022). Learning Based Image Classification Techniques. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-10766-5_3

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