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 dress 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 dress aesthetically, e.g., a red T-shirt matches better with white pants than with 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 "properly" criterion is described through a feature-occasion potential and an attribute-occasion potential, while the "aesthetically" criterion is expressed by an attribute-attribute potential.