Supervised neighborhood preserving embedding for feature extraction and its application for soft sensor modeling

Yuan, Xiaofeng; Ge, Zhiqiang; Ye, Lingjian; Song, Zhihuan

doi:10.1002/cem.2811

Cited by 12 publications

(9 citation statements)

References 25 publications

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“…Second, using color features alone cannot obtain good detection results. If the color of the clothing worn by the player is very close to the color of the field, the close-up shot of the player will also be considered a field shot [20].…”

Section: Common Lens Detection Methods For Tennis Courtsmentioning

confidence: 99%

Classification of Tennis Video Types Based on Machine Learning Technology

Gong

Wang

2021

Wireless Communications and Mobile Computing

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With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content-based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color-coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large-scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.

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Section: Common Lens Detection Methods For Tennis Courtsmentioning

confidence: 99%

Classification of Tennis Video Types Based on Machine Learning Technology

Gong

Wang

2021

Wireless Communications and Mobile Computing

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“…With improvements in SAR image resolution, detailed information of the image is obvious, and texture features of the building area are more abundant and applied to the information extraction of a high-resolution SAR image. Zhao, GAO, and Kuang [4] used the variation function to calculate the texture features of SAR images and applied the facial recognition and facial clustering, image indexing, and image classification [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. Bao et al presented the supervised NPE for feature extraction, using a class label to define the new distance to find the k nearest neighbors [43].…”

Section: Introductionmentioning

confidence: 99%

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Cheng

Cui

et al. 2020

IJGI

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Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.unsupervised fuzzy mean classification method to extract the building area. Texture feature images and filter images obtained by wavelet transform, according to Zhu et al. [5], are used as an input layer of the BP (Back Propagation) neural network, and the texture classification of high-resolution SAR images is completed. However, in the field of radar remote sensing, a new synthetic aperture radar (SAR) satellite constellation has been successfully launched in recent years. As SAR data have shown explosive growth, the SAR big data era has been flourishing. High-resolution polarimetric synthetic aperture radar (referred to as PolSAR) images can obtain ground scene information from multiple dimensions, on the one hand to provide rich information for identification of features and on the other hand to increase the complexity of information extraction. In the context of big data, rapid and accurate automatic extraction technology is the future development trend and is of great significance in promoting the application of PolSAR.Feature extraction is the most critical and central part of information extraction on SAR images. Studying the feature extraction method for high-resolution PolSAR images, reducing the space dimension and removing redundant information are highly important for the fast and accurate automatic extraction of building areas. Traditional feature extraction methods based on the global linear structure hypothesis premise feature of a PolSAR image set are proposed. Although they can achieve the purpose of reducing dimension, the intrinsic structure of high-dimension...

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“…Song et al 35 proposed a novel process monitoring in the Tennessee Eastman (TE) process via enhanced NPE. Yuan et al 36 proposed a supervised NPE method for feature extraction and soft sensor modeling to improve the control of debutanizer column. Miao et al 37 utilized the neighborhood preserving regression embedding (NPRE) method for nonlinear process soft sensor modeling to monitor fermentation process for penicillin production.…”

Section: Introductionmentioning

confidence: 99%

Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

Tan

Miao

et al. 2019

Measurement and Control

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This study aims to solve the problem involving the high false alarm rate experienced during the detection process when using the traditional multivariate statistical process monitoring method. In addition, the existing model cannot be updated according to the actual situation. This article proposes a novel adaptive neighborhood preserving embedding algorithm as well as an online fault-detection approach based on adaptive neighborhood preserving embedding. This approach combines the approximate linear dependence condition with neighborhood preserving embedding. According to the newly proposed update strategy, the algorithm can achieve an adaptive update model that realizes the online fault detection of processes. The effectiveness and feasibility of the proposed approach are verified by experiments of the Tennessee Eastman process. Theoretical analysis and application experiment of Tennessee Eastman process demonstrate that in this article proposed fault-detection method based on adaptive neighborhood preserving embedding can effectively reduce the false alarm rate and improve the fault-detection performance.

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Supervised neighborhood preserving embedding for feature extraction and its application for soft sensor modeling

Cited by 12 publications

References 25 publications

Classification of Tennis Video Types Based on Machine Learning Technology

Classification of Tennis Video Types Based on Machine Learning Technology

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

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