Apparel-based deep learning system design for apparel style recommendation

Guan, C. ORCID: 0000-0001-6374-8321, Qin, S. and Long, Y., 2019. Apparel-based deep learning system design for apparel style recommendation. International Journal of Clothing Science and Technology, 31 (3), pp. 376-389. ISSN 0955-6222

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Abstract

Purpose: The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert.

Design/methodology/approach: This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion.

Findings: The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable.

Originality/value: The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

Item Type: Journal article
Publication Title: International Journal of Clothing Science and Technology
Creators: Guan, C., Qin, S. and Long, Y.
Publisher: Emerald
Date: 2019
Volume: 31
Number: 3
ISSN: 0955-6222
Identifiers:
NumberType
10.1108/ijcst-02-2018-0019DOI
Divisions: Schools > School of Art and Design
Depositing User: Linda Sullivan
Date Added: 01 Aug 2019 08:45
Last Modified: 01 Aug 2019 08:45
URI: http://irep.ntu.ac.uk/id/eprint/37171

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