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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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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