Abstract
Automatic logo recognition is gaining importance due to the increasing number of its applications. Unlike other object recognition tasks, logo recognition is more challenging because of the limited amount of the available original data. In this paper, the transfer leaning technique was applied to a Deep Convolutional Neural Network model to guarantee logo recognition using a small computational overhead. The proposed method was based on the Densely Connected Convolutional Networks (DenseNet). The experimental results show that for the FlickrLogos-32 logo recognition dataset, our proposed method performs comparably with state-of-the-art methods while using fewer parameters.
Publisher
Engineering, Technology & Applied Science Research
Reference19 articles.
1. [1] R. Boia, A. Bandrabur, and C. Florea, "Local description using multi-scale complete rank transform for improved logo recognition," in 2014 10th International Conference on Communications (COMM), May 2014, pp. 1-4.
2. [2] S. Bianco, M. Buzzelli, D. Mazzini, and R. Schettini, "Deep Learning for Logo Recognition," Neurocomputing, Jan. 2017.
3. [3] F. N. Iandola, A. Shen, P. Gao, and K. Keutzer, "DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer," arXiv:1510.02131 [cs], Oct. 2015, Accessed: Aug. 12, 2020. [Online]. Available: http://arxiv.org/abs/1510.02131.
4. [4] C. Eggert, A. Winschel, and R. Lienhart, "On the Benefit of Synthetic Data for Company Logo Detection," in Proceedings of the 23rd ACM international conference on Multimedia, Oct. 2015, pp. 1283-1286.
5. [5] S. Bianco, M. Buzzelli, D. Mazzini, and R. Schettini, "Logo Recognition Using CNN Features," in Image Analysis and Processing - ICIAP 2015, Cham, 2015, pp. 438-448.
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献