Texture feature extraction and classification for CT image of xinjiang local liver hydatid based on support vector machine

2015 ◽  
pp. 395-400
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Texture Feature ◽  
Support Vector ◽  
Ct Image ◽  
Texture Feature Extraction

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

2020 ◽  
Vol 39 (4) ◽  
pp. 4847-4858
Author(s):  
Lei Wang ◽  
Jinhai Sun ◽  
Tuojian Li
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Feature Recognition ◽  
Texture Feature ◽  
Texture Features ◽  
Spatial Domain ◽  
Image Texture ◽  
Support Vector ◽  
Texture Feature Extraction

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.

scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
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>

scholarly journals Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

2021 ◽  
Vol 5 (2) ◽  
pp. 161-175
Author(s):  
Lutfi Hakim ◽  
Sepyan Purnama Kristanto ◽  
Dianni Yusuf ◽  
Mohammad Nur Shodiq ◽  
Wahyu Ade Setiawan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Texture Feature ◽  
Extraction Methods ◽  
Support Vector ◽  
Identification System ◽  
Accuracy Score ◽  
K Nearest Neighbors ◽  
Dragon Fruit ◽  
Combined Features

Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.

scholarly journals Comparative Analysis of Texture Patterns on Mammograms for Classification

2021 ◽  
Vol 38 (2) ◽  
pp. 379-386
Author(s):  
Nagadevi Darapureddy ◽  
Nagaprakash Karatapu ◽  
Tirumala Krishna Battula
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Texture Feature ◽  
Gradient Boosting ◽  
Support Vector ◽  
Texture Feature Extraction ◽  
Support Vector Classifier ◽  
Tree Classifier

Breast cancer is a cancerous tumor that arrives within the tissues of the breast. Women are mostly attacked than men. To detect early cancer medical specialists, suggest mammography for screening. Algorithms in Machine learning were executed on mammogram images to classify whether the tissues are deleterious or not. An analysis is done based on the texture feature extraction using different techniques like Frequency decoded local binary pattern (FDLBP), Local Bit-plane Decoded Pattern (LBDP), Local Diagonal Extrema Pattern (LDEP), Local Directional Order Pattern (LDOP), Local Wavelet Pattern (LWP). The features extracted are tested on 322 images from MIA’s database of three different classes. The algorithms in Machine learning like K-Nearest Neighbor classifier (KNN), Support vector classifier (SVC), Decision Tree classifier (DTC), Random Forest classifier (RFC), AdaBoost classifier (AC), Gradient Boosting classifier (GBC), Gaussian Naive Bayes classifier (GNB), Linear Discriminant Analysis classifier (LDA), Quadratic Discriminant Analysis classifier (QDA) were used to evaluate the accuracy of classification. This paper examines the comparison of accuracy using different texture features. KNN algorithm with LDEP for texture feature extraction gives classification accuracy of 64.61%, SVC with all the texture patterns mentioned gives classification accuracy of 63.07%, DTC with FDLBP, LBDP gives classification accuracy of 47.69, RFC with LBDP and AC with LDOP and GBC with FDLBP gives 61.53% classification accuracy, GNB and LDA with FDLBP gives 60% and 63.07% classification accuracy respectively, QDA with LBDP gives 64.61 classification accuracy. Of all the Algorithms support vector classifier gives good accuracy results with all the texture patterns mentioned.

scholarly journals 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

2021 ◽  
Vol 11 (5) ◽  
pp. 2332
Author(s):  
Stefania Barburiceanu ◽  
Romulus Terebes ◽  
Serban Meza
Keyword(s):  
Feature Extraction ◽  
Imaging Techniques ◽  
Texture Feature ◽  
Local Binary Patterns ◽  
Image Feature ◽  
Support Vector ◽  
Volumetric Data ◽  
Texture Feature Extraction ◽  
Discrimination Power ◽  
Local Anisotropy

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Sign in / Sign up

Export Citation Format

Share Document