Segment Augmentation and Differentiable Ranking for Logo Retrieval

Logo retrieval is a challenging problem since the definition of similarity is more subjective than image retrieval, and the set of known similarities is very scarce. In this paper, to tackle this challenge, 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 strategies that perform augmentation at the image level. Moreover, we evaluate suitability of using ranking-based losses (namely Smooth-AP) for learning similarity for logo retrieval. On the METU and the LLD datasets, 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.