Please use this identifier to cite or link to this item:
Title: Hi, magic closet, tell me what to wear!
Authors: Liu, S. 
Nguyen, T.V.
Feng, J.
Wang, M.
Yan, S. 
Keywords: latent SVM
occasion oriented clothing pairing
Issue Date: 2012
Citation: Liu, S.,Nguyen, T.V.,Feng, J.,Wang, M.,Yan, S. (2012). Hi, magic closet, tell me what to wear!. MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia : 1333-1334. ScholarBank@NUS Repository.
Abstract: In this demo, we present a practical system, magic closet, for automatic occasion-oriented clothing pairing. Given a user-input occasion, e.g., wedding or shopping, the magic closet intelligently and automatically pairs the user-specified reference clothing (upper-body or lower-body) with the most suitable one from online shops. Two key criteria are explicitly considered for the magic closet system. One criterion is to wear properly, e.g., compared to suit pants, it is more decent to wear a cocktail dress for a banquet occasion. The other criterion is to wear aesthetically, e.g., a red T-shirt matches better white pants than green pants. To narrow the semantic gap between the low-level visual features and the high-level occasion categories, we propose to adopt middle-level clothing attributes (e.g., clothing category, color, pattern) as a bridge. More specifically, the clothing attributes are treated as latent variables in our proposed latent Support Vector Machine (SVM) based recommendation model. The wearing properly criterion is described through a feature-occasion potential and an attribute-occasion potential, while the wearing aesthetically criterion is expressed by an attribute-attribute potential. © 2012 Authors.
Source Title: MM 2012 - Proceedings of the 20th ACM International Conference on Multimedia
ISBN: 9781450310895
DOI: 10.1145/2393347.2396470
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Dec 9, 2018

Page view(s)

checked on Dec 8, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.