No Reference Quality Assessment for Screen Content Images Using Stacked Autoencoders in Pictorial and Textual Regions

Yang, Jiachen; Zhao, Yang; Liu, Jiacheng; Jiang, Bin; Meng, Qinggang; Lu, Wen; Gao, Xinbo

doi:10.1109/tcyb.2020.3024627

Cited by 61 publications

(34 citation statements)

References 59 publications

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“…e spectral clustering algorithm is based on the division of spectral graphs, and its essence is to transform the clustering problem into the optimal segmentation problem of graphs [32][33][34][35]. e spectral clustering algorithm regards the data samples as the vertices in the graph, which is represented by the set J, the vertices are connected by edges, and the edges are represented by the set B.…”

Section: Spectral Clustering Algorithmmentioning

confidence: 99%

Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm

Chen

Wen-hao

2021

Complexity

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Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the research on the spectral clustering algorithm is still in the early stage of development. Many existing algorithms are very sensitive to clustering parameters, especially scale parameters, and need to manually input the number of clustering. Therefore, a density-sensitive similarity measure is introduced in this paper, which is obtained by introducing new parameters to transform the Gaussian function. Under this metric, the distance between data points belonging to different classes will be effectively amplified, while the distance between data points belonging to the same class will be reduced, and finally, the distribution of data will be effectively clustered. At the same time, the idea of Eigen gap is introduced into the spectral clustering algorithm, and the verified gap sequence is constructed on the basis of Laplace matrix, so as to solve the problem of the number of initial clustering. The strong global search ability of artificial bee colony algorithm is used to make up for the shortcoming of spectral clustering algorithm that is easy to fall into local optimal. The experimental results show that the adaptive spectral clustering algorithm can better identify the initial clustering center, perform more effective clustering, and detect abnormal behavior more accurately.

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Section: Spectral Clustering Algorithmmentioning

confidence: 99%

Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm

Chen

Wen-hao

2021

Complexity

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“…After filtering the image, we must also evaluate the image quality [20]. Image quality mainly includes two aspects: one is the degree of difference between the image and the original standard image; the other is the ability of the image to provide information to individuals or machines from subjective and 4…”

Section: Filter Effect Evaluation and Results Analysismentioning

confidence: 99%

Key Technologies of Steel Plate Surface Defect Detection System Based on Artificial Intelligence Machine Vision

Xue

2021

Wireless Communications and Mobile Computing

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With the rapid development of visual inspection technology, computer technology, and image processing technology, machine vision technology has become more and more mature, and the role of quality inspection and control in the steel industry is becoming more and more obvious and important. Defects on the surface of the strip are a key factor affecting the quality inspection process. Its inspection plays an extremely important role in improving the final quality. For a long time, traditional manual inspection methods cannot meet actual production needs, so in-depth research on steel surface defect inspection systems has become the consensus of today’s steel companies. The accuracy and low performance of traditional detection methods can no longer meet the needs of people and society. The surface defect detection method based on machine vision has the characteristics of high accuracy, fast processing speed, and intelligent processing, which is the main trend of surface defect detection. We select a steel plate; take the invariant moment features of the cracks, holes, scratches, oil stains, and other images on it; extract the data results; and analyze them. Then, we read the texture features of these defect images again, extract the data results, and analyze them. The experimental results prove that after the mean value filter and Gaussian filter process the image, the mean variance value MSE is relatively large ( 46.276 > 31.2271 ), and as the concentration of salt and pepper noise increases, the rate of increase of MSE increases obviously, and as the peak signal-to-noise ratio and the mean variance value MSE increase continuously ( 32.2271 < 33.3695 ), the image distortion is more serious. The method designed in this paper is extremely effective. Improving the surface quality of steel is of great significance to improving market competitiveness.

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“…In this paper, the model is initialized and trained so that the parameters of the convolutional layer can be shared, and also to improve the effect of using random initialization alone [35,36]. e research in this paper adopts the following optimizations for the training of the model.…”

Section: Model Parameter Designmentioning

confidence: 99%

Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

2021

Complexity

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This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.

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No Reference Quality Assessment for Screen Content Images Using Stacked Autoencoders in Pictorial and Textual Regions

Cited by 61 publications

References 59 publications

Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm

Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm

Key Technologies of Steel Plate Surface Defect Detection System Based on Artificial Intelligence Machine Vision

Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design

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