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Image classification of fine-grained fashion image based on style using pre-trained convolutional neural network

Title
Image classification of fine-grained fashion image based on style using pre-trained convolutional neural network
Authors
Seo Y.Shin K.-S.
Ewha Authors
신경식
SCOPUS Author ID
신경식scopus
Issue Date
2018
Journal Title
2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018
Citation
2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018, pp. 387 - 390
Keywords
Convolutional Neural Networkfashion imagefine-grained classificationpre-trained network
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCOPUS scopus
Document Type
Conference Paper
Abstract
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.
DOI
10.1109/ICBDA.2018.8367713
ISBN
9781538647936
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
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