Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
ACM SIGGRAPH 2006 Papers
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
What am I gonna wear?: scenario-oriented recommendation
Proceedings of the 12th international conference on Intelligent user interfaces
Chromirror: a real-time interactive mirror for chromatic and color-harmonic dressing
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Large margin training for hidden Markov models with partially observed states
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Web image mining towards universal age estimator
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Automatic attribute discovery and characterization from noisy web data
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A discriminative latent model of object classes and attributes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Detecting people using mutually consistent poselet activations
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Predicting facial beauty without landmarks
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
News contextualization with geographic and visual information
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Fashion coordinates recommender system using photographs from fashion magazines
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Predicting occupation via human clothing and contexts
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Describing people: A poselet-based approach to attribute classification
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi, magic closet, tell me what to wear!
Proceedings of the 20th ACM international conference on Multimedia
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
AteGau: projector-based online fashion coordination system
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Fashion-focused creative commons social dataset
Proceedings of the 4th ACM Multimedia Systems Conference
"Wow! you are so beautiful today!"
Proceedings of the 21st ACM international conference on Multimedia
"Wow! you are so beautiful today!"
Proceedings of the 21st ACM international conference on Multimedia
"Real, but Glossy": technology and the practical pursuit of magic in modern weddings
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
MOWL: An ontology representation language for web-based multimedia applications
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Fashion 10000: an enriched social image dataset for fashion and clothing
Proceedings of the 5th ACM Multimedia Systems Conference
Hi-index | 0.00 |
In this paper, we aim at a practical system, magic closet, for automatic occasion-oriented clothing recommendation. Given a user-input occasion, e.g., wedding, shopping or dating, magic closet intelligently suggests the most suitable clothing from the user's own clothing photo album, or 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 features of clothing and the high-level occasion categories, we 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 in the model through a feature-occasion potential and an attribute-occasion potential, while the wearing aesthetically criterion is expressed by an attribute-attribute potential. To learn a generalize-well model and comprehensively evaluate it, we collect a large clothing What-to-Wear (WoW) dataset, and thoroughly annotate the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk. Extensive experiments on the WoW dataset demonstrate the effectiveness of the magic closet system for both occasion-oriented clothing recommendation and pairing.