Almgren Khaled, Krishnan Murali, Aljanobi Fatima, Lee Jeongkyu
College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia.
College of Engineering, University of Bridgeport, Bridgeport, CT 06614, USA.
Entropy (Basel). 2018 Dec 17;20(12):982. doi: 10.3390/e20120982.
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.
由于深度学习的发展，多媒体数据的处理与分析已成为一个热门研究课题。深度学习在解决诸多具有挑战性的问题方面发挥了重要作用，如计算机视觉、图像识别和图像检测等，这些在许多实际应用中都很有用。在本研究中，我们分析了图像的视觉特征，以从各类杂志的扫描图像中检测广告图像。目的是识别广告图像的关键特征并将其应用于实际应用。所提出的工作最终将有助于改进营销策略，这需要对杂志中的广告图像进行分类。我们使用卷积神经网络将扫描图像分类为广告或非广告（即文章）。结果表明，所提出的方法在准确性方面优于其他分类器和相关工作。
