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Comparison of Deep Learning, Data Augmentation and Bag of-Visual-Words for Classification of Imbalanced Image Datasets

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1035))

Abstract

Image classification is a supervised machine learning task to classify images into different categories. As most real-world datasets are imbalanced in nature, instances are not equally distributed in all classes which often results in biased classification. So considering this objective, we are dealing with an imbalanced dataset. We have created a small size of an imbalanced dataset consisting of Ant and Plane Images from Caltech-101 dataset. This paper illustrates the comparison between Deep learning, Data Augmentation and the Bag-of-Visual Words (BOVW) for classification of imbalanced image datasets. According to the experimental results, it was found that deep learning results in higher accuracy in comparison to bag-of-visual-words (BOVW) and data augmentation.

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Author information

Authors and Affiliations

  1. Delhi Technological University, Delhi, India

    Manisha Saini & Seba Susan

Authors
  1. Manisha Saini
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  2. Seba Susan
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Corresponding author

Correspondence to Manisha Saini .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Saini, M., Susan, S. (2019). Comparison of Deep Learning, Data Augmentation and Bag of-Visual-Words for Classification of Imbalanced Image Datasets. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_49

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  • DOI: https://doi.org/10.1007/978-981-13-9181-1_49

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9180-4

  • Online ISBN: 978-981-13-9181-1

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