In this paper, we demonstrate a practical system for automatic weather-oriented clothing suggestion, given the weather information, the system can automatically recommend the most suitable clothing from the user s personal clothing album, or intelligently suggest the most pairing one with the user-specified reference clothing. This is an extremely challenging problem due to the large discrepancy factors that should be considered under different weather conditions. To approach this task, we use clothing attributes as a mid-level bridge to narrow the gap between low-level features and the high-level weather categories. We adopt a scoring function, which includes three terms, to model the relationship. To acquire an optimized model and verify our proposed method, we collect a large clothing Weather-to-Garment (WoG) dataset. Experiments on the WoG dataset demonstrate that our learned model are effective for both weather-oriented clothing recommendation and pairing.
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