Scalable logo detection by self co-learning

PATTERN RECOGNITION

卷:97

Article

Su, Hang¹  Gong, Shaogang¹  Zhu, Xiatian² 

[1] Queen Mary Univ London, London E1 4NS, England

[2] Vis Semant Ltd, London E1 4NS, England

关键词: Object detection;    Logo recognition;    Logo dataset;    Web data mining;    Self-Learning;    Co-Learning;   

DOI  :  10.1016/j.patcog.2019.107003

来源: Elsevier

【 摘 要 】

Existing logo detection methods usually consider a small number of logo classes, limited images per class and assume fine-gained object bounding box annotations. This limits their scalability to real-world dynamic applications. In this work, we tackle these challenges by exploring a web data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL2), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner. Moreover, we introduce a very large (2,190,757 images of 194 logo classes) logo dataset WebLogo-2M by designing an automatic data collection and processing method. Extensive comparative evaluations demonstrate the superiority of SL2 over the state-of-the-art strongly and weakly supervised detection models and contemporary web data learning approaches. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free