Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

Zhao, Ziting; Liu, Tong; Zhao, Xudong

doi:10.1155/2021/5538573

Cited by 3 publications

(4 citation statements)

References 23 publications

(33 reference statements)

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“…where i represents the total amount of color channel of the painted image, and p ij represents the color value of the i-th color channel of the j-th pixel [10]. Texture features: Wavelet analysis is the most widely used method in extracting image texture features [11]. is method is very stable in signal processing and has shown good results in many research fields, with broad application prospects.…”

Section: Image Feature Extraction and Fusionmentioning

confidence: 99%

Research on Painting Image Classification Based on Transfer Learning and Feature Fusion

Yong

2022

Mathematical Problems in Engineering

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In order to effectively solve the problems of high error rate, long time consuming, and low accuracy of feature extraction in current painting image classification methods, a painting image classification method based on transfer learning and feature fusion was proposed. The global characteristics of the painting picture, such as color, texture, and form, are extracted. The SIFT method is used to extract the painting’s local features, and the global and local characteristics are normalized and merged. The painting images are preliminarily classified using the result of feature fusion, the deterministic and nondeterministic samples are divided, and the estimated Gaussian model parameters are transferred to the target domain via a transfer learning algorithm to alter the distribution of nondeterministic samples, completing the painting image classification. Experimental results show that the proposed method has a low error rate and low feature extraction time and a high accuracy rate of painting image classification.

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Section: Image Feature Extraction and Fusionmentioning

confidence: 99%

Research on Painting Image Classification Based on Transfer Learning and Feature Fusion

Yong

2022

Mathematical Problems in Engineering

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“…Moreover, since the extracted features are represented as numerical data in this study, the application of SVM can be highly appropriate. It is because SVM has been proven to be a capable tool for classifying extracted numerical datasets [19,[36][37][38][39].…”

Section: Research Background and Motivationmentioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

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“…Abdelkader et al [11] developed an entropy-based automated approach for detection and assessment of spalling severities in reinforced concrete bridges; invasive weed optimization-based image segmentation, information theory-based formalism of images, and the Elman neural network are hybridized to formulate the proposed method. Zhao et al [12] investigated various feature selection strategies used with machine learning models and texture descriptors to detect concrete surface voids.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, although machine learning methods have been extensively used in computer vision-based structural health monitoring [3,12,[24][25][26], hybrid approaches that combine the strengths of machine learning and metaheuristic algorithms are rarely investigated in this field especially for concrete spall recognition. Metaheuristic algorithms can be used to optimize the learning phase of machine learning models and therefore help to achieve better predictive performances [27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

Hoang

Huynh

Tran

2021

Advances in Civil Engineering

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During the phase of building survey, spalling and its severity should be detected as earlier as possible to provide timely information on structural heath to building maintenance agency. Correct detection of spall severity can significantly help decision makers develop effective maintenance schedule and prioritize their financial resources better. This study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. Based on input image of concrete surface, the method is capable of distinguishing between a minor spalling in which the depth of the broken-off material is less than the concrete cover layer and a deep spalling in which the reinforcing steel bars have been revealed. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. A dataset consisting of 300 image samples has been collected to train and verify the proposed computer vision method. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate = 93.33%, F1 score = 0.93, and area under the receiver operating characteristic curve = 0.97.

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Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

Cited by 3 publications

References 23 publications

Research on Painting Image Classification Based on Transfer Learning and Feature Fusion

Research on Painting Image Classification Based on Transfer Learning and Feature Fusion

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

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