AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines
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Authors
Almgren, Khaled
Krishnan, Murali
Aljanobi, Fatima
Lee, Jeongkyu
Issue Date
2018-12-17
Type
Article
Language
en_US
Keywords
Deep learning , Convolutional neural network , Image recognition , Advertisement detection
Alternative Title
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.
Description
Citation
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.
Publisher
MDPI