Convolutional Neural Network Based Advertisement Classification Models for Online English Newspapers

Abstract

Image processing for knowledge management and effective information extraction is the key element for steering towards society 5.0. There has been a substantial research and progress in the area of image recognition and classification in the recent years but at the same time, there is a lack of significant work in the field of advertisement image classification from online English newspapers. This research paper analyses and compares various popular image classification techniques to find out the most suitable technique for advertisement image classification problem. Automatic feature extraction without any prior knowledge of features makes Convolutional Neural Networks (CNN) the most suitable technique for advertisement image classification. This paper further explores and implements three different CNN-based image classification models that can classify advertisement images from online English newspapers into four pre-defined categories including Admission-notices, Job-advertisements, Sales and Promotional advertisements and Tenders. These models are trained and tested on an advertisement image dataset collected from four different online English newspapers over a time frame of 15 months. Fine-tuned ResNet50 Model using ‘Transfer-learning’ is found to be the most suitable model for this advertisement image classification task with results exhibiting around 74% accuracy.  This CNN-model based automated classification of advertisement images will help newspaper readers in performing exhaustive advertisement search in a category of their own interest, saving the time and efforts of sequential manual search across a range of multiple newspapers. Also, the proposed research will help in performing advertisement analysis and studies.