Journal of Engineering and Applied Sciences

Year: 2019
Volume: 14
Issue: 18
Page No. 6897 - 6905

Trademark Image Retrieval using Transfer Learning

Authors : Shahla J. Hassen, Ahmed Taha and Mazen M. Selim

References

Aires, S.B.K., C.O.A. de Freitas and L.S. Oliveira, 2015. SIFT applied to perceptual zoning for trademark retrieval. Proceedings of the 2015 IEEE International Conference on Systems, Man and Cybernetics, October 9-12, 2015, IEEE, Kowloon, China, ISBN:978-1-4799-8697-2, pp: 2401-2406.

Anonymus, 2018. Datasets: FlickrLogos-32/FlickrLogos-47. University of Augsburg, Augsburg, Germany. http://www.multimedia-computing.de/flickrlogos/#flic%20krlogos47

Anuar, F.M., R. Setchi and Y.K. Lai, 2013. Trademark image retrieval using an integrated shape descriptor. Exp. Syst. Appl., 40: 105-121.
Direct Link  |  

Anuar, F.M., R. Setchi and Y.K. Lai, 2016. Semantic retrieval of trademarks based on conceptual similarity. IEEE. Trans. Syst. Man Cybern. Syst., 46: 220-233.
CrossRef  |  Direct Link  |  

Bianco, S., M. Buzzelli, D. Mazzini and R. Schettini, 2017. Deep learning for logo recognition. Neurocomputing, 245: 23-30.
CrossRef  |  Direct Link  |  

Donahue, J., Y. Jia, O. Vinyals, J. Hoffman and N. Zhang et al., 2014. Decaf: A deep convolutional activation feature for generic visual recognition. Proceedings of the 31st International Conference on Machine Learning (PMLR) Vol. 32, June 21-26, 2014, Beijing, China, pp: 647-655.

Her, I., K. Mostafa and H.K. Hung, 2011. A hybrid trademark retrieval system using four-gray-level zernike moments and image compactness indices. Intl. J. Image Proc., 4: 631-646.
Direct Link  |  

Iandola, F.N., A. Shen, P. Gao and K. Keutzer, 2015. Deeplogo: Hitting logo recognition with the deep neural network hammer. Comput. Vision Pattern Recogn., 1: 1-12.
Direct Link  |  

Kensert, A., P.J. Harrison and O. Spjuth, 2018. Transfer learning with deep convolutional neural networks for classifying cellular morphological changes. Slas Discovery Adv. Life Sci., 1: 1-10.
CrossRef  |  PubMed  |  Direct Link  |  

Kochakornjarupong, P., 2001. Trademark image retrieval by local features. Ph.D Thesis, College of Social Sciences, University of Glasgow, Scotland, UK.

Krizhevsky, A., I. Sutskever and G.E. Hinton, 2012. Image net classification with deep convolutional neural networks. Proc. Neural Inf. Process. Syst., 1: 1097-1105.

Laiwen, Y., S. Ping and L. Yanhong, 2014. Design and implementation of trademark image retrieval system. Proceedings of the 2014 International Conference on Audio, Language and Image Processing, July 7-9, 2014, IEEE, Shanghai, China, ISBN:978-1-4799-3902-2, pp: 160-165.

Liu, Y., J. Wang, Z. Li and H. Li, 2017. Efficient logo recognition by local feature groups. Multimedia Syst., 23: 395-403.
Direct Link  |  

Oliveira, G., X. Frazao, A. Pimentel and B. Ribeiro, 2016. Automatic graphic logo detection via fast region-based convolutional networks. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), July 24-29, 2016, IEEE, Vancouver, British Columbia, Canada, ISBN:978-1-5090-0621-2, pp: 985-991.

Phan, R. and D. Androutsos, 2010. Content-based retrieval of logo and trademarks in unconstrained color image databases using color edge gradient Co-occurrence histograms. Comput. Vision Image Understanding, 114: 66-84.
CrossRef  |  Direct Link  |  

Pinjarkar, L., M. Sharma and S. Selot, 2018. Novel relevance feedback approach for color trademark recognition using optimization and learning strategy. J. Intell. Syst., 27: 67-79.
CrossRef  |  

Sharif Razavian, A., H. Azizpour, J. Sullivan and S. Carlsson, 2014. CNN features off-the-shelf: An astounding baseline for recognition. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPR), June 23-28, 2014, Columbus, Ohio, USA., pp: 806-813.

Simonyan, K. and A. Zisserman, 2014. Very deep convolutional networks for large-scale image recognition. J. Comput. Vision Pattern Recognit., 1: 1-14.
Direct Link  |  

Szegedy, C., W. Liu, Y. Jia, P. Sermanet and S. Reed et al., 2015. Going deeper with convolutions. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2015) Vol. 2, June 7-12, 2015, Boston, Massachusetts, USA., pp: 1-9.

Tursun, O. and S. Kalkan, 2015. METU dataset: A big dataset for benchmarking trademark retrieval. Proceedings of the 2015 14th IAPR International Conference on Machine Vision Applications (MVA), May 18-22, 2015, IEEE, Tokyo, Japan, ISBN:978-4-9011-2214-6, pp: 514-517.

Tursun, O., C. Aker and S. Kalkan, 2017. A large-scale dataset and benchmark for similar trademark retrieval. Comput. Vision Pattern Recogn., 1: 1-33.
Direct Link  |  

Wei, C.H., Y. Li, W.Y. Chau and C.T. Li, 2009. Trademark image retrieval using synthetic features for describing global shape and interior structure. Pattern Recogn., 42: 386-394.
CrossRef  |  Direct Link  |  

Yan, Y., J. Ren, Y. Li, J.F.C. Windmill and W. Ijomah et al., 2016. Adaptive fusion of color and spatial features for noise-robust retrieval of colored logo and trademark images. Multidimension. Syst. Signal Proc., 27: 945-968.
CrossRef  |  Direct Link  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved