Multi-label classification of line chart images using convolutional neural networks

Kösemen, Cem; Birant, Derya

doi:10.1007/s42452-020-3055-y

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…The line and bar charts are the most researched. Kosmen et al [16] created a CNN architecture that can classify line charts according to trend property (increasing or decreasing) and functional property (linear or exponential). The achieved average classification accuracy is 93.76%.…”

Section: Related Workmentioning

confidence: 99%

Chart Classification Using Siamese CNN

Bajić

Job

2021

J. Imaging

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In recovering information from the chart image, the first step should be chart type classification. Throughout history, many approaches have been used, and some of them achieve results better than others. The latest articles are using a Support Vector Machine (SVM) in combination with a Convolutional Neural Network (CNN), which achieve almost perfect results with the datasets of few thousand images per class. The datasets containing chart images are primarily synthetic and lack real-world examples. To overcome the problem of small datasets, to our knowledge, this is the first report of using Siamese CNN architecture for chart type classification. Multiple network architectures are tested, and the results of different dataset sizes are compared. The network verification is conducted using Few-shot learning (FSL). Many of described advantages of Siamese CNNs are shown in examples. In the end, we show that the Siamese CNN can work with one image per class, and a 100% average classification accuracy is achieved with 50 images per class, where the CNN achieves only average classification accuracy of 43% for the same dataset.

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Section: Related Workmentioning

confidence: 99%

Chart Classification Using Siamese CNN

Bajić

Job

2021

J. Imaging

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“…Label powerset transforms MDC problems into multi-class classification problems by combining all unique class labels in the training set as a new single label [43]. As a result of this process, each instance in the training set has only one target attribute with one class label.…”

Section: Label Powersetmentioning

confidence: 99%

An Ordinal Multi-Dimensional Classification (OMDC) for Predictive Maintenance

Taşer¹

2023

Computer Systems Science and Engineering

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Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed. Although machine learning techniques have been frequently implemented in this area, the existing studies disregard to the natural order between the target attribute values of the historical sensor data. Thus, these methods cause losing the inherent order of the data that positively affects the prediction performances. To deal with this problem, a novel approach, named Ordinal Multi-dimensional Classification (OMDC), is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values. To demonstrate the prediction ability of the proposed approach, eleven different multi-dimensional classification algorithms (traditional Binary Relevance (BR), Classifier Chain (CC), Bayesian Classifier Chain (BCC), Monte Carlo Classifier Chain (MCC), Probabilistic Classifier Chain (PCC), Classifier Dependency Network (CDN), Classifier Trellis (CT), Classifier Dependency Trellis (CDT), Label Powerset (LP), Pruned Sets (PS), and Random k-Labelsets (RAKEL)) were implemented using the Ordinal Class Classifier (OCC) algorithm. Besides, seven different classification algorithms (Multilayer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Decision Tree (C4.5), Bagging, Random Forest (RF), and Adaptive Boosting (AdaBoost)) were chosen as base learners for the OCC algorithm. The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy. Also, it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

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“…Chagas et al [ 28 ] compared listed methods and showed that CNNs outperform by roughly 20%. Publications by Bajić and Job [ 11 ], Kosemen and Birant [ 29 ], Ishihara et al [ 30 ], and Dadhich et al [ 31 ] use custom CNN architectures for chart-type classification. CNNs can also be used out-of-the-box; some are available as pre-trained models.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

Bajić

Orel

Habijan

2022

Sensors

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Charts are often used for the graphical representation of tabular data. Due to their vast expansion in various fields, it is necessary to develop computer algorithms that can easily retrieve and process information from chart images in a helpful way. Convolutional neural networks (CNNs) have succeeded in various image processing and classification tasks. Nevertheless, the success of training neural networks in terms of result accuracy and computational requirements requires careful construction of the network layers’ and networks’ parameters. We propose a novel Shallow Convolutional Neural Network (SCNN) architecture for chart-type classification and image generation. We validate the proposed novel network by using it in three different models. The first use case is a traditional SCNN classifier where the model achieves average classification accuracy of 97.14%. The second use case consists of two previously introduced SCNN-based models in parallel, with the same configuration, shared weights, and parameters mirrored and updated in both models. The model achieves average classification accuracy of 100%. The third proposed use case consists of two distinct models, a generator and a discriminator, which are both trained simultaneously using an adversarial process. The generated chart images are plausible to the originals. Extensive experimental analysis end evaluation is provided for the classification task of seven chart classes. The results show that the proposed SCNN is a powerful tool for chart image classification and generation, comparable with Deep Convolutional Neural Networks (DCNNs) but with higher efficiency, reduced computational time, and space complexity.

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Multi-label classification of line chart images using convolutional neural networks

Cited by 9 publications

References 29 publications

Chart Classification Using Siamese CNN

Chart Classification Using Siamese CNN

An Ordinal Multi-Dimensional Classification (OMDC) for Predictive Maintenance

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

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