Color Navigation by Qualitative Attributes for Fashion Recommendation
This paper proposes a novel method to navigate a color palette using attributes recognized from speech input. Our target application is a fashion recommender system for mobile e-commerce. Starting with a selected color, a user can request to show items of a different color by qualitative attributes (e.g. `a little cuter’). These attributes are mapped to a query vector within the Lab color space in order to select the next color. The system distinguishes 85 attributes, each with three different possible magnitudes. This color navigation by speech was demonstrated in a mobile fashion recommender system. The proposed model is validated in a user study with 196 subjects.
Research Areas : #Vision Program
Careers : Open Positions