Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

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A cluster-based feature selection method for image texture classification

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v14.i3.pp1433-1442 ◽

2019 ◽

Vol 14 (3) ◽

pp. 1433 ◽

Cited By ~ 2

Author(s):

Abbas F. H. Alharan ◽

Hayder K. Fatlawi ◽

Nabeel Salih Ali

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Clustering Algorithm ◽

Gabor Filter ◽

Texture Feature ◽

Image Feature ◽

Image Texture ◽

Support Vector ◽

Texture Image ◽

Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

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Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix

International Journal of Virtual Reality ◽

10.20870/ijvr.2011.10.3.2823 ◽

2011 ◽

Vol 10 (3) ◽

pp. 73-79 ◽

Cited By ~ 1

Author(s):

Jian Yang ◽

Jingfeng Guo

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Texture Feature ◽

Local Area ◽

Image Texture ◽

Gray Level ◽

Texture Feature Extraction ◽

Feature Extraction Method ◽

Level Difference ◽

Occurrence Matrix

Texture feature is a measure method about relationship among the pixels in local area, reflecting the changes of image space gray levels. This paper presents a texture feature extraction method based on regional average binary gray level difference co-occurrence matrix, which combined the texture structural analysis method with statistical method. Firstly, we calculate the average binary gray level difference of eight-neighbors of a pixel to get the average binary gray level difference image which expresses the variation pattern of the regional gray levels. Secondly, the regional co-occurrence matrix is constructed by using these average binary gray level differences. Finally, we extract the second-order statistic parameters reflecting the image texture feature from the regional co-occurrence matrix. Theoretical analysis and experimental results show that the image texture feature extraction method has certain accuracy and validity

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Bispectrum Texture Feature Manifold for Feature Extraction in Rolling Bear Fault Diagnosis

Mathematical Problems in Engineering ◽

10.1155/2019/3805729 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Fei Wang ◽

Liqing Fang

Keyword(s):

Feature Extraction ◽

Dimensionality Reduction ◽

Texture Feature ◽

Texture Features ◽

Rolling Bearing ◽

Image Texture ◽

Support Vector ◽

Contour Map ◽

Low Dimensional ◽

Degradation Degree

Effectively classify the fault types and the degradation degree of a rolling bearing is an important basis for accurate malfunction detection. A novel feature extract method - bispectrum image texture features manifold (BTM) of the rolling bearing vibration signal is proposed in this paper. The BTM method is realized by three main steps: bispectrum image analysis, texture feature construction and manifold feature dimensionality reduction. In this method, bispectrum analysis is employed to convert the mass vibration signals into bispectrum contour map, the typical texture features were extracted from the contour map by gray level co-occurrence matrix (GLCM), then the manifold dimensionality reduction method liner local tangent space alignment (LLTSA) is used to remove redundant information and reduce the dimension from the extracted texture features and obtain more meaningful low-dimensional information. Furthermore, the low-dimensional texture features were identified by support vector machine (SVM) which was optimized by genetic optimization algorithm (GA). The validity of BTM is confirmed by rolling bear experiments, the result show that the proposed feature extraction method can accurately distinguish different fault types and have a good performance to classify the degradation degree of inner race fault, outer race fault and rolling ball fault.

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A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

Remote Sensing ◽

10.3390/rs11141636 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1636 ◽

Cited By ~ 6

Author(s):

Xudong Lai ◽

Jingru Yang ◽

Yongxu Li ◽

Mingwei Wang

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Land Management ◽

Extraction Method ◽

Point Cloud ◽

Texture Feature ◽

Texture Features ◽

Extraction Methods ◽

Building Extraction ◽

Texture Feature Extraction

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

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Classification of Tumor of MRI Brain Image Using Hybrid Feature Extraction Method and Support Vector Machine Classifier

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3842 ◽

2021 ◽

Vol 11 (10) ◽

pp. 2558-2565

Author(s):

K. Kavinkumar ◽

T. Meeradevi

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Extraction Method ◽

Support Vector Machine Classifier ◽

Texture Features ◽

Support Vector ◽

Svm Classifier ◽

Tumor Type ◽

Feature Extraction Method ◽

Hybrid Feature Extraction

Brain tumors Analysis is problematic somewhat due to varied size, shape, location of tumor and the appearance and presence of brain tumor. Clinicians and radiologist have difficulty in identifying the tumor type. An efficient hybrid feature extraction method to classify the type of tumor accurately as meningioma, gliomas and pituitary tumor using SVM (support vector machine) classifier is proposed. The modified Non-Local Means (NLM) filter may be effectively used to get the pure image. The NLM filter is compared with common filters like median and wiener. From the denoised image the classification is done by training SVM using the texture features from the hybrid and efficient feature extraction technique.The accuracy of the classification is calculated and the SVM classifier training individual type of texture features and also with combined texture features and the performance is analyzed.

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