Comparison of Multi-Label Learning Approaches for Pie Chart Image Classification Using Deep Learning

Authors : Derya BIRANT, Cem KÖSEMEN

Pages : 116-124

View : 20 | Download : 17

Publication Date : 2021-12-30

Article Type : Research Paper

Abstract :A pie chart is a powerful and circular information graphic used to display numerical proportions to the whole. However, the properties of pie charts cannot be directly noticed by machines since they are usually in an image format. To make a pie chart classifiable by machines, this paper proposes a novel solution using deep learning methods. This study is original in that it automatically and jointly classifies charts in terms of two respects: shape insert ignore into journalissuearticles values(pie or donut); and dimension insert ignore into journalissuearticles values(2D or 3D);. This is the first study that compares two multi-label learning approaches to classify pie charts: binary-class-based convolutional neural networks insert ignore into journalissuearticles values(BCNN); and multi-class- based convolutional neural networks insert ignore into journalissuearticles values(MCNN);. The experimental results showed that the BCNN model achieved 86% accuracy, while the MCNN model reached 85% accuracy on real-world pie chart data.
Keywords : Convolutional neural networks, deep learning, image classification, machine learning, multi label learning, pie charts