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Entropy 2018, 20(12), 982; https://doi.org/10.3390/e20120982

AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines

1
College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
2
College of Engineering, University of Bridgeport, Bridgeport, CT 06614, USA
*
Author to whom correspondence should be addressed.
Received: 4 December 2018 / Revised: 13 December 2018 / Accepted: 16 December 2018 / Published: 17 December 2018
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Abstract

The processing and analyzing of multimedia data has become a popular research topic due to the evolution of deep learning. Deep learning has played an important role in addressing many challenging problems, such as computer vision, image recognition, and image detection, which can be useful in many real-world applications. In this study, we analyzed visual features of images to detect advertising images from scanned images of various magazines. The aim is to identify key features of advertising images and to apply them to real-world application. The proposed work will eventually help improve marketing strategies, which requires the classification of advertising images from magazines. We employed convolutional neural networks to classify scanned images as either advertisements or non-advertisements (i.e., articles). The results show that the proposed approach outperforms other classifiers and the related work in terms of accuracy. View Full-Text
Keywords: deep learning; convolutional neural network; image recognition; advertisement detection; advertisement detection deep learning; convolutional neural network; image recognition; advertisement detection; advertisement detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Almgren, K.; Krishnan, M.; Aljanobi, F.; Lee, J. AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines. Entropy 2018, 20, 982.

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