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Clothing Classification Using Deep CNN Architecture Based on Transfer Learning

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Hybrid Intelligent Systems (HIS 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1179))

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Abstract

The need of a powerful visual analytics tools becomes a necessity today especially with the emergence of pictures on the Internet and their use several times instead of text. In this paper, a new approach for clothing style classification is presented. The types of clothing items we consider in the proposed system include shirt, pants, suit, dress and so on. Certainly, clothing style classification represents a recent computer vision research subject who has several attractive applications, including e-commerce, criminal law and on-line advertising. In our proposed approach, the classification has been carried out by Deep Convolutional Neural Networks (CNNs). This Deep Learning technique Inception-v3 has shown very good performances for different object recognition problems. For deep features extraction, we use a machine learning technique called Transfer learning to refine pretrained models. Experiments are performed on two clothing datasets, particularly on the large and public dataset ImageNet. According to the obtained results, the developed system provides better results than those proposed in the state of the art.

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Author information

Authors and Affiliations

  1. National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia

    Mohamed Elleuch

  2. Higher Institute of Industrial Management, University of Sfax, Sfax, Tunisia

    Anis Mezghani

  3. Faculty of Sciences, University of Sfax, Sfax, Tunisia

    Mohamed Elleuch, Mariem Khemakhem & Monji Kherallah

Authors
  1. Mohamed Elleuch
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  2. Anis Mezghani
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  3. Mariem Khemakhem
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  4. Monji Kherallah
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Corresponding author

Correspondence to Mohamed Elleuch .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. School of Computer Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India

    Shishir K. Shandilya

  3. Area of Project Engineering, University of Cordoba, Córdoba, Spain

    Laura Garcia-Hernandez

  4. Escola de Engenharia, Universidade do Minho, Guimarães, Portugal

    Maria Leonilde Varela

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Elleuch, M., Mezghani, A., Khemakhem, M., Kherallah, M. (2021). Clothing Classification Using Deep CNN Architecture Based on Transfer Learning. In: Abraham, A., Shandilya, S., Garcia-Hernandez, L., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2019. Advances in Intelligent Systems and Computing, vol 1179. Springer, Cham. https://doi.org/10.1007/978-3-030-49336-3_24

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