Retrieval-based methodology for few-sample logo recognition

Logo recognition describes the challenging task of detecting and classifying logos in digital images and videos. Former works approach logo recognition as a closed-set problem. However, this approach is accompanied by several shortcomings linked with its incapability of recognising new classes. In this paper, we propose an open-set logo recognition method, named REtrieval-based methodology For FEw-sample LOgo Recognition (REFELOR). REFELOR is composed by a generic logo detector and a feature extractor, allowing the generalization on unseen classes, using only a few samples per logo. That is, a single-stage generic logo detector is trained to detect logos in an input image. Then, feature representations for the detected logos are extracted, using the feature extractor, while the feature representations of a database containing only a few samples per class are also extracted. Finally, the detected logo representations are classified to the corresponding class based a similarity search in the representations of the aforementioned database. In addition, a regularization technique is applied to the feature extractor, providing further improvements. The experimental evaluation validates the effectiveness of the proposed method, outperforming current state-of-the-art logo recognition methods.