论文标题
徽标检索的细分市场扩展和可区分排名
Segment Augmentation and Differentiable Ranking for Logo Retrieval
论文作者
论文摘要
徽标检索是一个具有挑战性的问题，因为与图像检索任务相比，相似性的定义更为主观，并且已知相似性的集合非常稀缺。为了应对这一挑战，在本文中，我们提出了一种简单但有效的基于细分市场的增强策略，以引入人工相似的徽标，以训练徽标检索的深层网络。在这种新颖的增强策略中，我们首先在徽标中找到细分市场，并在细分市场上应用旋转，缩放和颜色变化等转换，这与传统的图像级增强策略不同。此外，我们评估了最近引入的基于排名的损失函数Smooth-AP是否是学习徽标检索相似性的更好方法。在大规模的METU商标数据集上，我们表明（i）与基线模型或图像级增强策略相比，基于细分市场的增强策略可以提高检索性能，并且（ii）平滑-AP的确比徽标检索的常规损失更好。
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.
