Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques

Hassan Zaidi, Syed Muhammad and Jamil Alsayaydeh, Jamil Abedalrahim and Khan, Abdul Hafeez and Khan, Abdullah Ayub and AlZubi, Ahmad Ali and Ogunshola, Benny and Herawan, Safarudin Gazali (2025) Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques. PeerJ Computer Science, 7. pp. 1-25. ISSN 2376-5992

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Abstract

Visualization through charts has become a crucial method for presenting complex data in a clear and structured format, surpassing traditional representation techniques in both clarity and interpretability. However, the automatic classification of chart images remains a significant challenge, particularly in the absence of labeled data. This study investigates the unsupervised classification of chart images using a combination of deep learning and clustering techniques. The primary objective is to develop a method capable of automatically categorizing four common chart types, histogram, bar, line, and pie charts without relying on annotated datasets. These chart types are frequently used in critical domains such as financial, socio-economic, and political analysis. To achieve this, a pre-trained Visual Geometry Group 16 (VGG16) model is employed for feature extraction, followed by principal component analysis (PCA) for dimensionality reduction. The k-means clustering algorithm is then applied to group visually similar chart images. For classification, three pre-trained models, Residual Network 50 (ResNet50), Residual Network 50 Version 2 (ResNet50V2), and Densely Connected Convolutional Network (DenseNet) are evaluated alongside a customized convolutional neural network (CNN) using the ChartVQA dataset. The models achieve classification accuracy of 85.40%, 80.76%, 82.46%, and 90.71%, respectively. By integrating transfer learning with clustering and CNN-based architecture, this study presents a robust and scalable framework for unsupervised chart image classification.

Item Type:	Article
Uncontrolled Keywords:	Charts, Image, Vgg16, Restnet50, Transfer learning, K-means clustering, Unsupervised, Machine learning, Image classification, Convolutional neural network
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	23 Feb 2026 05:50
Last Modified:	23 Feb 2026 05:50
URI:	http://eprints.utem.edu.my/id/eprint/29573
Statistic Details:	View Download Statistic

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