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Towards color compatibility in fashion using machine learning
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Fashion analyses, such as predicting trends and fashion recommendation, have been a hot topic. Color, as one of the dominant features of clothing, has great influence on people’s shopping behaviors. Understanding popular colors and color combinations are of high business value. In this thesis, we investigate compatible color combinations in fashion. We tackle this problem in two parts. First, we implement a semantic segmentation model of fashion images to segment different clothing items of daily photos. We employ Deeplab V2 trained on ModaNet dataset, reaching 0.64 mIoU and 0.96 accuracy in the test set. Our experimental results achieve the state-of-the-art performance comparing to other models proposed in this field. Second, we propose two color recommendation approaches, matrix factorization and item-to-item collaborative filtering, in order to study color combinations in fashion and possibly make recommendations based on the study outcomes. The item-to-item collaborative filtering model shows the compatibility between/among colors quantitatively and achieves high-quality color recommendations with a hit-rate of 0.49.

Abstract [sv]

Modeanalyser,som att förutse trender och mode, är ett hett område. Färg, som är en av de dominerande egenskaperna hos kläder,har stor inverkan på människors shoppingbeteenden. Att förstå populära färger och färgkombinationer är av högt aärsvärde. I denna avhandling undersöker vi kompatibla färgkombinationer inom mode. Vi tar itu med detta problem i två delar. Först genomför vi en semantisk segmenteringsmodell av modebilder för att segmentera olika klädselar av modebilder.Våra experimentella resultat visar att vår segmenteringsmodell når topp-prestanda och är mer generaliserbar jämfört med andra modeller som föreslås inom detta fält. Därför föreslår vi två färgrekommendationsmetoder; matrisfaktorisering och sammansatt ltrering mellan objekt och objekt. Detta i syfte att studera färgkombinationer inom mode och möjligengöra färgrekommendationer. Våra experiment visar kompatibilitet mellan färger kvantitativt och uppnår färgrekommendationer med en träffsäkerhet på 0.49.

Place, publisher, year, edition, pages
2019. , p. 50
Series
TRITA-EECS-EX ; 2019:531
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-257784OAI: oai:DiVA.org:kth-257784DiVA, id: diva2:1348501
External cooperation
Norna AB
Supervisors
Examiners
Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-09-04Bibliographically approved

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