Feature Extraction and Classification of Images Based on Corner Invariant Moments

2013 ◽  
Vol 475-476 ◽  
pp. 374-378
Author(s):  
Xue Ming Zhai ◽  
Dong Ya Zhang ◽  
Yu Jia Zhai ◽  
Ruo Chen Li ◽  
De Wen Wang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Image Feature ◽  
Support Vector ◽  
System Management ◽  
Image Feature Extraction ◽  
Invariant Moments ◽  
Harris Corner ◽  
Classification Of Images

Image feature extraction and classification is increasingly important in all sectors of the images system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates, so can reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of images, and the result shows that using this algorithm to extract invariant moments and classifying can achieve better classification accuracy.

scholarly journals A Novel Multi-Feature Fusion Method in Merging Information of Heterogenous-View Data for Oil Painting Image Feature Extraction and Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Tong Chen ◽  
Juan Yang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
Image Feature ◽  
Support Vector ◽  
Fusion Method ◽  
Feature Classification ◽  
Image Feature Extraction ◽  
Oil Painting ◽  
The Way

The art of oil painting reflects on society in the form of vision, while technology constantly explores and provides powerful possibilities to transform the society, which also includes the revolution in the way of art creation and even the way of thinking. The progress of science and technology often provides great changes for the creation of art, and also often changes people's way of appreciation and ideas. The oil painting image feature extraction and recognition is an important field in computer vision, which is widely used in video surveillance, human-computer interaction, sign language recognition and medical, health care. In the past few decades, feature extraction and recognition have focused on the multi-feature fusion method. However, the captured oil painting image is sensitive to light changes and background noise, which limits the robustness of feature extraction and recognition. Oil painting feature extraction is the basis of feature classification. Feature classification based on a single feature is easily affected by the inaccurate detection accuracy of the object area, object angle, scale change, noise interference and other factors, resulting in the reduction of classification accuracy. Therefore, we propose a novel multi-feature fusion method in merging information of heterogenous-view data for oil painting image feature extraction and recognition in this paper. It fuses the width-to-height ratio feature, rotation invariant uniform local binary mode feature and SIFT feature. Meanwhile, we adopt a modified faster RCNN to extract the semantic feature of oil painting. Then the feature is classified based on the support vector machine and K-nearest neighbor method. The experiment results show that the feature extraction method based on multi-feature fusion can significantly improve the average classification accuracy of oil painting and have high recognition efficiency.

Classification of Lettuce Nitrogen Levels Based on Image Feature Extraction and Optimization

2013 ◽  
Vol 12 (23) ◽  
pp. 7574-7579 ◽  
Author(s):  
Sun Jun ◽  
Jiang Shuying ◽  
Mao Hanping ◽  
Zhang Xiaodong ◽  
Zhu Wenjing ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Nitrogen Levels

Lumbar Ultrasound Image Feature Extraction and Classification with Support Vector Machine

2015 ◽  
Vol 41 (10) ◽  
pp. 2677-2689 ◽  
Author(s):  
Shuang Yu ◽  
Kok Kiong Tan ◽  
Ban Leong Sng ◽  
Shengjin Li ◽  
Alex Tiong Heng Sia
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Ultrasound Image ◽  
Image Feature ◽  
Support Vector ◽  
Image Feature Extraction

Electromyography (EMG) based Classification of Finger Movements using SVM

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Nurazrin Mohd Esa ◽  
Azlan Mohd Zain ◽  
Mahadi Bahari
Keyword(s):  
Feature Extraction ◽  
Control System ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Myoelectric Control ◽  
Support Vector ◽  
Zero Crossing ◽  
Single Root ◽  
Artificial Hand

Myoelectric control prostheses hand are currently popular developing clinical option that offers amputee person to control their artificial hand by analyzing the contacting muscle residual. Myoelectric control system contains three main phase which are data segmentation, feature extraction and classification. The main factor that affect the performance of myoelectric control system is the choice of feature extraction methods. There are two types of feature extraction technique used to extract the signal which are the Hudgins feature consist of Zero Crossing, Waveform Length (WL), Sign Scope Change (SSC) and Mean Absolute Value (MAV), the single Root Mean Square (RMS). Then, the combination of both is proposed in this study. An analysis of these different techniques result were examine to achieve a favorable classification accuracy (CA). Our outcomes demonstrate that the combination of RMS and Hudgins feature set demonstrate the best average classification accuracy for all ten fingers developments. The classification process implemented in this studies is using Support Vector Machine (SVM) technique.

scholarly journals Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Qingqing Lu ◽  
Jiexin Pu ◽  
Zhonghua Liu
Keyword(s):  
Feature Extraction ◽  
Ground Penetrating Radar ◽  
Classification Accuracy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Noisy Environment ◽  
Feature Based ◽  
Approximation Coefficients ◽  
Ground Penetrating

Ground penetrating radar (GPR) is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT) transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT) is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM) classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.

scholarly journals WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes

2021 ◽  
Vol 17 (13) ◽  
pp. 135-150
Author(s):  
Najla Alofi ◽  
Wafa Alonezi ◽  
Wedad Alawad
Keyword(s):  
Feature Extraction ◽  
Blood Cells ◽  
Classification Accuracy ◽  
White Blood Cells ◽  
Hybrid Models ◽  
Learning Technologies ◽  
Support Vector ◽  
Svm Model ◽  
Vector Machines

Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.

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