Image classification of fine-grained fashion image based on style using pre-trained convolutional neural network
- 주제(키워드) Convolutional Neural Network , fashion image , fine-grained classification , pre-trained network
- 등재 SCOPUS
- 발행기관 Institute of Electrical and Electronics Engineers Inc.
- 발행년도 2018
- URI http://www.dcollection.net/handler/ewha/000000155830
- ISBN 9781538647936
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/ICBDA.2018.8367713
초록/요약
Deep learning has emerged as a new methodology with continuous interests in artificial intelligence, and it can be applied in various business fields for better performance. In fashion business, deep learning, especially Convolutional Neural Network (CNN), is used in classification of apparel image. However, apparel classification can be difficult due to various apparel categories and lack of labeled image data for each category. Therefore, we propose to pre-train the GoogLeNet architecture on ImageNet dataset and fine-tune on our fine-grained fashion dataset based on design attributes. This will complement the small size of dataset and reduce the training time. After 10-fold experiments, the average final test accuracy results 62%. © 2018 IEEE.
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