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Implementation of CNN-based Classification Training Model for Unstructured Fashion Image Retrieval using Preprocessing with MASK R-CNN
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비정형 패션 이미지 검색을 위한 MASK R-CNN 선형처리 기반 CNN 분류 학습모델 구현

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Type
Academic journal
Author
Seunga Cho (덕성여자대학교) Hayoung Lee (덕성여자대학교) Hyelim Jang (덕성여자대학교) Kyuri Kim (덕성여자대학교) Hyeon-Ji Lee (덕성여자대학교) Bong-Ki Son (서원대학교) Jaeho Lee (덕성여자대학교)
Journal
Korea Society of Industrial Informantion Systems Journal of the Korea Industrial Information Systems Research Vol.27 No.6 KCI Accredited Journals
Published
2022.12
Pages
13 - 23 (11page)

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

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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%.
#비정형 데이터 #이미지 분류 #신경망 #Unstructured Data #Image Classification #Neural Network #Mask R-CNN

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요약
Abstract
1. 서론
2. 연구배경
3. 모델 설계
4. 성능 분석
5. 결론
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