Aggregating Everyday Outfits by Incremental Clustering With Interactive User Adaptation

Abstract

Knowledge of the outfits that a person wears daily and how frequently the person wears them will help the person select clothing every morning. However, it is very-time consuming to manually record what the person wears every day. This paper proposes a system that automatically aggregates and visualizes the outfits of a user by using a monitoring camera at home. To aggregate the everyday outfits of a user, we employ incremental clustering. For accurate clustering, an appropriate feature space is required. However, there is a gap between the clothing feature space of people and a specific user. To fill the gap, we propose a Siamese-network based interactive user adaptation method using user feedback. The user adaptation incrementally updates the similarity metric of the clothing feature space. We confirmed that the proposed system achieves highly accurate clustering performance with a smaller amount of user feedback through evaluation.

Keywords

• Clothing frequency
• fashion application
• incremental clustering
• metric learning
• user adaptation
• user feedback