Enhancing Apparel Data Based on Fashion Theory for Developing a Novel Apparel Style Recommendation System

Guan, Congying, Qin, Sheng-feng, Ling, Wessie and Long, Yang (2018) Enhancing Apparel Data Based on Fashion Theory for Developing a Novel Apparel Style Recommendation System. In: Trends and Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, 747 . Springer, pp. 31-40. ISBN 978-3-319-77699-6

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Abstract

Smart apparel recommendation system is a kind of machine learning system applied to clothes online shopping. The performance quality of the system is greatly dependent on apparel data quality as well as the system learning ability. This paper proposes (1) to enhance knowledge-based apparel data based on fashion communication theories and (2) to use deep learning driven methods for apparel data training. The acquisition of new apparel data is supported by apparel visual communication and sign theories. A two-step data training model is proposed. The first step is to predict apparel ATTRIBUTEs from the image data through a multi-task CNN model. The second step is to learn apparel MEANINGs from predicted attributes through SVM and LKF classifiers. The testing results show that the prediction rate of eleven predefined MEANING classes can reach the range from 80.1% to 93.5%. The two-step apparel learning model is applicable for novel recommendation system developments.

Item Type:	Book Section
Uncontrolled Keywords:	Apparel recommendation, Body, Style, Visual communication system, Apparel data Deep learning
Subjects:	G400 Computer Science W200 Design studies
Department:	Faculties > Arts, Design and Social Sciences > Design
Depositing User:	Becky Skoyles
Date Deposited:	30 Apr 2018 13:43
Last Modified:	11 Oct 2019 21:00
URI:	http://nrl.northumbria.ac.uk/id/eprint/34109

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