TY  - JOUR
T1  - Trademark Image Retrieval using Transfer Learning
AU - Hassen, Shahla J. AU - Taha, Ahmed AU - Selim, Mazen M. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 18
SP  - 6897
EP  - 6905
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.6897.6905
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.6897.6905
KW  - Content-based image retrieval
KW  -trademark image retrieval
KW  -deep learning
KW  -transfer learning
KW  -AlexNet
KW  -academically
AB  - Trademarks are valuable assets that need to be protected from infringement for the sake of producers
and consumers. Therefore, Trademark Image Retrieval (TIR) is getting an increasing attention both academically
and commercially. Recently, convolutional neural networks have stand out as a compulsory alternate. It offers
perfect predictive performance and the possibility to replace classical workflows with an only network
architecture. In addition, the transfer learning can save time and efforts in building deep convolutional neural
networks. In this study, a transfer learning based TIR system is presented. It employs AlexNet, a pre-trained
deep convolutional neural network. The proposed system is evaluated and validated using the two benchmark
datasets: "FlickrLogos32" and "Logos-32 Plus" in terms of well-known performance metrics. The obtained
results show that our proposed system has a promising performance compared to other recent systems.
ER  -