Historical documents dating using multispectral imaging and ordinal classification

Rahiche, Abderrahmane; Hedjam, Rachid; Al-Maadeed, Somaya; Cheriet, Mohamed

doi:10.1016/j.culher.2020.01.012

Cited by 16 publications

(10 citation statements)

References 20 publications

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“…This section presents the experimental results in terms of the experimental setup and performance results for the SVM-RBF and SVM-Linear, NDCI [ 8 ], HSRS [ 19 ], SCMask R-CNN [ 17 ], CIAs [ 18 ], KCA [ 21 ], AOPC [ 22 ], MLC [ 33 ] and MDC [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

“…The proposed approach is called as SCMask R-CNN, and the goal of this proposed approach is to enhance the detection effect by providing a higher resolution of remote image sensing.The proposed approach also provided segmentation and object recognition concurrently. In [ 8 ], the authors have focused on the discoloration and modification in the optical processes. Thus, new non-destructive and content independent (NDCI) methods are proposed for ranking classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SVM-RBF, SVM-Linear, MLC, and MDC have been compared both from a mathematical perspective and with the experiment results to show their different levels of accuracy and efficiency. Moreover, SVM-RBF, SVM-Linear have also been compared with current state-of-the-art algorithms: NDCI [ 8 ], SCMask R-CNN [ 17 ], CIAs [ 18 ], KCA [ 21 ], and AOPC [ 22 ] from the change detection accuracy, and reliability viewpoint.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Compared with field investigation, RS technology is much more efficient and cheaper in terms of time and cost [ 7 ]. RS image classification is a significant part of the overall field of RS, which can be thought of as a joint venture between both image processing and classification techniques [ 8 ]. The classification can be implemented by algorithms that are either supervised or unsupervised: the former uses pre-labeled data and the latter uses data without labeling.…”

Section: Introductionmentioning

confidence: 99%

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Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Razaque

Frej

Almiani

et al. 2021

Sensors

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Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Razaque

Frej

Almiani

et al. 2021

Sensors

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“…Ordinal classification (OC) has been studied by machine learning researchers in many different fields such as environment [17], finance [18], history [19], health, and transportation. However, there is no study reported in the literature combining HAR and ordinal classification.…”

Section: Related Workmentioning

confidence: 99%

Ordered physical human activity recognition based on ordinal classification

Daş¹,

Birant²

2021

Turk J Elec Eng & Comp Sci

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Human activity recognition (HAR) is a critical process for applications that focus on the classification of human physical activities such as jogging, walking, downstairs, and upstairs. Ordinal classification (OC) is a special type of supervised multi-class classification in which an inherent ordering among the classes exists, such as low, medium, and high. This study combines these two concepts and introduces an approach to "human activity recognition based on ordinal classification" (HAROC). In the proposed approach, ordinal classification is applied to human activity recognition where the physical activities can be ordered by using their signals' band power values. This is the first study that investigates the performance of the HAROC approach by combining the ordinal classification with eight different base learners.Besides, this study is also original in that it examines the effects of the demographic characteristics of the participants (i.e., gender, age, weight, and height) on the classification performance. The experiments carried out on a real-world dataset show that the proposed HAROC approach is an effective method for human activity recognition tasks.

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