Learning an Order Preserving Image Similarity through Deep Ranking

Recently, deep learning frameworks have been shown to learn a feature embedding that captures fine-grained image similarity using image triplets or quadruplets that consider pairwise relationships between image pairs. In real-world datasets, a class contains fine-grained categorization that exhibits within-class variability. In such a scenario, these frameworks fail to learn the relative ordering between - (i) samples belonging to the same category, (ii) samples from a different category within a class and (iii) samples belonging to a different class. In this paper, we propose the quadlet loss function, that learns an order-preserving fine-grained image similarity by learning through quadlets (query, positive