  Logo retrieval is a challenging problem since the definition of similarity is
more subjective compared to image retrieval tasks and the set of known
similarities is very scarce. To tackle this challenge, in this paper, we
propose a simple but effective segment-based augmentation strategy to introduce
artificially similar logos for training deep networks for logo retrieval. In
this novel augmentation strategy, we first find segments in a logo and apply
transformations such as rotation, scaling, and color change, on the segments,
unlike the conventional image-level augmentation strategies. Moreover, we
evaluate whether the recently introduced ranking-based loss function,
Smooth-AP, is a better approach for learning similarity for logo retrieval. On
the large scale METU Trademark Dataset, we show that (i) our segment-based
augmentation strategy improves retrieval performance compared to the baseline
model or image-level augmentation strategies, and (ii) Smooth-AP indeed
performs better than conventional losses for logo retrieval.
