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Evaluation of Several Artificial Intelligence and Machine Learning Algorithms for Image Classification on Small Datasets

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Advances on Smart and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1188))

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Abstract

In this paper, a benchmark of machine learning (ML) algorithms for single-label image classification is proposed and evaluated on a small dataset. The dataset is obtained through a mobile application allowing citizens to upload images related to water and electricity distribution infrastructure problems. The collected dataset is preprocessed, organized and used to train and evaluate classical supervised ML algorithms (SVM, NB, DT, KNN and MLP) along with deep learning methods (CNN and transfer learning). Data augmentation and fine-tuning techniques are explored to handle the overfitting problem. Conducted experiment results show the effectiveness of transfer learning with data augmentation and fine-tuning using the VGG16 network as the precision reaches 89%.

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Authors and Affiliations

  1. Mathematics and Informatics Department, LIMSAD Laboratory, Ain Chok Sciences College, Hassan II University, Casablanca, Morocco

    Rim Koulali, Hajar Zaidani & Maryeme Zaim

Authors
  1. Rim Koulali
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  2. Hajar Zaidani
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  3. Maryeme Zaim
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Corresponding author

Correspondence to Rim Koulali .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. Department of Computing and Technology, Nottingham Trent University, Nottingham, UK

    Tawfik Al-Hadhrami

  3. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Malaysia

    Fathey Mohammed

  4. Faculty of science Ain Chock, Hassan II University of Casablanca, Casablanca, Morocco

    Errais Mohammed

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Koulali, R., Zaidani, H., Zaim, M. (2021). Evaluation of Several Artificial Intelligence and Machine Learning Algorithms for Image Classification on Small Datasets. In: Saeed, F., Al-Hadhrami, T., Mohammed, F., Mohammed, E. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1188. Springer, Singapore. https://doi.org/10.1007/978-981-15-6048-4_5

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