Classification of Images on Furniture and Household Goods by Using Transfer Leaning and Fine Tuning

  • Authors

    • Kingshuk Das Bakshi
    • Dr. Gagan Deep
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.25.26933
  • Convolutional neural networks (CNN), texture classification, transfer learning, computer vision.

  • Abstract

    Automatic product recognition for shoppers in online shopping is a challenging task. The reason behind that is, for the same product, a picture can be taken in different intensities of light, angles, backgrounds and levels of occlusion. This  causes the different fine-grained categories look very similar. Many of general-purpose recognition machines used now days, cannot perceive such subtle differences between photos. These differences could be important for shopping decisions. In this paper, a novel approach has been proposed based on deep learning and artificial neural networks (ANN) for pattern recognition, which accurately assigns category labels for furniture and home good images. This is done by classification of textual patterns, to help push state of art in automatic image classification. In deep learning, transfer learning is used, where two pre trained convolutional neural network (CNN) models are retrained. The CNN models used for this experiment are VGG-16 and Inception V3. The experiment is carried on dataset taken from kaggle and classification is made among five items named bed, sofa, table, chair and swivel chair. The experimental results are measured by performance metrics, in terms of training accuracy, validation accuracy, training loss and validation loss. The  results demonstrate that the accuracy of  Inception V3 transfer learning model with 97.3% is more than  VGG-16 and ANN with accuracy of 92% and 86%, respectively.

     

     

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  • How to Cite

    Das Bakshi, K., & Gagan Deep, D. (2018). Classification of Images on Furniture and Household Goods by Using Transfer Leaning and Fine Tuning. International Journal of Engineering & Technology, 7(4.25), 250-255. https://doi.org/10.14419/ijet.v7i4.25.26933

    Received date: 2019-01-31

    Accepted date: 2019-01-31

    Published date: 2018-11-30