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http://dx.doi.org/10.9723/jksiis.2022.27.6.013

Implementation of CNN-based Classification Training Model for Unstructured Fashion Image Retrieval using Preprocessing with MASK R-CNN  

Seunga, Cho (덕성여자대학교 소프트웨어전공)
Hayoung, Lee (덕성여자대학교 IT미디어공학전공)
Hyelim, Jang (덕성여자대학교 IT미디어공학전공)
Kyuri, Kim (덕성여자대학교 IT미디어공학전공)
Hyeon-Ji, Lee (덕성여자대학교 IT미디어공학전공)
Bong-Ki, Son (서원대학교 소프트웨어학부 컴퓨터공학전공)
Jaeho, Lee (덕성여자대학교 소프트웨어전공)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.27, no.6, 2022 , pp. 13-23 More about this Journal
Abstract
In this paper, we propose a detailed component image classification algorithm by fashion item for unstructured data retrieval in the fashion field. Due to the COVID-19 environment, AI-based online shopping malls are increasing recently. However, there is a limit to accurate unstructured data search with existing keyword search and personalized style recommendations based on user surfing behavior. In this study, pre-processing using Mask R-CNN was conducted using images crawled from online shopping sites and then classified components for each fashion item through CNN. We obtain the accuaracy for collar of the shirt's as 93.28%, the pattern of the shirt as 98.10%, the 3 classese fit of the jeans as 91.73%, And, we further obtained one for the 4 classes fit of jeans as 81.59% and the color of the jeans as 93.91%. At the results for the decorated items, we also obtained the accuract of the washing of the jeans as 91.20% and the demage of jeans accuaracy as 92.96%.
Keywords
Unstructured Data; Image Classification; Neural Network; Mask R-CNN;
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Times Cited By KSCI : 3  (Citation Analysis)
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