HiBsteR: Hierarchical Boosted Deep Metric Learning for Image Retrieval

When the number of categories is growing into thousands, large-scale image retrieval becomes an increasingly hard task. Retrieval accuracy can be improved by learning distance metric methods that separate categories in a transformed embedding space. Unlike most methods that utilize a single embedding to learn a distance metric, we build on the idea of boosted metric learning, where an embedding is split into a boosted ensemble of embeddings. While in general metric learning is directly applied on fine labels to learn embeddings, we take this one step further and incorporate hierarchical label information into the boosting framework and show how to properly adapt loss functions for this purpose. We show that by introducing several sub-embeddings which focus on specific hierarchical classes, the retrieval accuracy can be improved compared to standard flat label embeddings. The proposed method is especially suitable for exploiting hierarchical datasets or when additional labels can be retrieved without much effort. Our approach improves R@1 over state-of-the-art methods on the biggest available retrieval dataset (Stanford Online Products) and sets new reference baselines for hierarchical metric learning on several other datasets (CUB-200-2011, VegFru, FruitVeg-81). We show that the clustering quality in terms of NMI score is superior to previous works.