Logo Recognition with the Use of Deep Convolutional Neural Networks
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.
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. DOI: https://doi.org/10.1109/ICComm.2014.6866723
S. Bianco, M. Buzzelli, D. Mazzini, and R. Schettini, "Deep Learning for Logo Recognition," Neurocomputing, Jan. 2017. DOI: https://doi.org/10.1016/j.neucom.2017.03.051
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.
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. DOI: https://doi.org/10.1145/2733373.2806407
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. DOI: https://doi.org/10.1007/978-3-319-23234-8_41
S. Romberg and R. Lienhart, "Bundle min-hashing for logo recognition," in Proceedings of the 3rd ACM conference on International conference on multimedia retrieval, Apr. 2013, pp. 113-120. DOI: https://doi.org/10.1145/2461466.2461486
S. Romberg, L. G. Pueyo, R. Lienhart, and R. van Zwol, "Scalable logo recognition in real-world images," in Proceedings of the 1st ACM International Conference on Multimedia Retrieval, Apr. 2011, pp. 1-8. DOI: https://doi.org/10.1145/1991996.1992021
E. Francesconi et al., "Logo recognition by recursive neural networks," in Graphics Recognition Algorithms and Systems, 1998, pp. 104-117. DOI: https://doi.org/10.1007/3-540-64381-8_43
S. Duffner and C. Garcia, "A Neural Scheme for Robust Detection of Transparent Logos in TV Programs," in Artificial Neural Networks - ICANN 2006, 2006, pp. 14-23. DOI: https://doi.org/10.1007/11840930_2
G. Zhu and D. Doermann, "Automatic Document Logo Detection," in Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Sep. 2007, vol. 2, pp. 864-868. DOI: https://doi.org/10.1109/ICDAR.2007.4377038
G. Zhu and D. Doermann, "Logo Matching for Document Image Retrieval," in 2009 10th International Conference on Document Analysis and Recognition, Jul. 2009, pp. 606-610. DOI: https://doi.org/10.1109/ICDAR.2009.60
G. Oliveira, X. Frazão, A. Pimentel, and B. Ribeiro, "Automatic graphic logo detection via Fast Region-based Convolutional Networks," in 2016 International Joint Conference on Neural Networks (IJCNN), Jul. 2016, pp. 985-991. DOI: https://doi.org/10.1109/IJCNN.2016.7727305
T. Williams and R. Li, "An Ensemble of Convolutional Neural Networks Using Wavelets for Image Classification," Journal of Software Engineering and Applications, vol. 11, no. 2, pp. 69-88, Feb. 2018. DOI: https://doi.org/10.4236/jsea.2018.112004
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp. 2261-2269. DOI: https://doi.org/10.1109/CVPR.2017.243
O. Russakovsky et al., "ImageNet Large Scale Visual Recognition Challenge," International Journal of Computer Vision, vol. 115, no. 3, pp. 211-252, Dec. 2015. DOI: https://doi.org/10.1007/s11263-015-0816-y
Y. Jia et al., "Caffe: Convolutional Architecture for Fast Feature Embedding," in Proceedings of the 22nd ACM international conference on Multimedia, Nov. 2014, pp. 675-678. DOI: https://doi.org/10.1145/2647868.2654889
U. Khan, K. Khan, F. Hassan, A. Siddiqui, and M. Afaq, "Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4423-4427, Aug. 2019. DOI: https://doi.org/10.48084/etasr.2734
Y. Said, M. Barr, and H. E. Ahmed, "Design of a Face Recognition System based on Convolutional Neural Network (CNN)," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608-5612, Jun. 2020. DOI: https://doi.org/10.48084/etasr.3490
Y. F. Said and M. Barr, "Pedestrian Detection for Advanced Driver Assistance Systems using Deep Learning Algorithms," International Journal of Computer Science and Network Security, vol. 19, no. 9, pp. 9-14, Sep. 2019.
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