Age Classification Based on Feature Fusion

2014 ◽  
Vol 519-520 ◽  
pp. 644-650
Author(s):  
Mian Shui Yu ◽  
Yu Xie ◽  
Xiao Meng Xie
Keyword(s):  
Feature Fusion ◽  
Principal Component ◽  
Directional Pattern ◽  
Gabor Wavelet ◽  
Support Vector ◽  
Classification Problems ◽  
Pca Method ◽  
Multi Class Classification ◽  
Global And Local ◽  
Fusion Theory

Age classification based on facial images is attracting wide attention with its broad application to human-computer interaction (HCI). Since human senescence is a tremendously complex process, age classification is still a highly challenging issue. In our study, Local Directional Pattern (LDP) and Gabor wavelet transform were used to extract global and local facial features, respectively, that were fused based on information fusion theory. The Principal Component Analysis (PCA) method was used for dimensionality reduction of the fused features, to obtain a lower-dimensional age characteristic vector. A Support Vector Machine (SVM) multi-class classifier with Error Correcting Output Codes (ECOC) was proposed in the paper. This was aimed at multi-class classification problems, such as age classification. Experiments on a public FG-NET age database proved the efficiency of our method.

A Novel Classification Method of Fish Based on Multi-Feature Fusion

2015 ◽  
Vol 713-715 ◽  
pp. 1513-1519 ◽  
Author(s):  
Wei Dong Du ◽  
Bao Wei Chen ◽  
Hai Sen Li ◽  
Chao Xu
Keyword(s):  
Discrete Cosine Transform ◽  
Feature Fusion ◽  
Acoustic Scattering ◽  
Scattering Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Identification ◽  
Classification Problems ◽  
Cosine Transform ◽  
Identification Rate

In order to solve fish classification problems based on acoustic scattering data, temporal centroid (TC) features and discrete cosine transform (DCT) coefficients features used to analyze acoustic scattering characteristics of fish from different aspects are extracted. The extracted features of fish are reduced in dimension and fused, and support vector machine (SVM) classifier is used to classify and identify the fishes. Three kinds of different fishes are selected as research objects in this paper, the correct identification rates are given based on temporal centroid features and discrete cosine transform coefficients features and fused features. The processing results of actual experimental data show that multi-feature fusion method can improve the identification rate at about 5% effectively.

scholarly journals Assisted Diagnosis of Alzheimer’s Disease Based on Deep Learning and Multimodal Feature Fusion

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Wang ◽  
Xi Liu ◽  
Chongchong Yu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Support Vector ◽  
Image Size ◽  
Resonance Imaging

With the development of artificial intelligence technologies, it is possible to use computer to read digital medical images. Because Alzheimer’s disease (AD) has the characteristics of high incidence and high disability, it has attracted the attention of many scholars, and its diagnosis and treatment have gradually become a hot topic. In this paper, a multimodal diagnosis method for AD based on three-dimensional shufflenet (3DShuffleNet) and principal component analysis network (PCANet) is proposed. First, the data on structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. Then, the original two-dimensional (2D) ShuffleNet is developed three-dimensional (3D), which is more suitable for 3D sMRI data to extract the features. In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. Next, kernel canonical correlation analysis (KCCA) is used to fuse the features coming from sMRI and fMRI, respectively. Finally, a good classification effect is obtained through the support vector machines (SVM) method classifier, which proves the feasibility and effectiveness of the proposed method.

scholarly journals Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2215
Author(s):  
Jung-Kai Tsai ◽  
Chih-Hsing Hung
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Classification Problem ◽  
Back Propagation Neural Network ◽  
Future Research ◽  
Support Vector ◽  
Enterprise Performance ◽  
Classification Problems ◽  
Proposed Model ◽  
Multi Class Classification

Because COVID-19 occurred in 2019, the behavioxr of humans has been changed and it will influence the business model of enterprise. Enterprise cannot predict its development according to past knowledge and experiment; so, it needs a new machine learning framework to predict enterprise performance. The goal of this research is to modify AdaBoost to reasonably predict the enterprise performance. In order to justify the usefulness of the proposed model, enterprise data will be collected and the proposed model can be used to predict the enterprise performance after COVID-19. The test data correct rate of the proposed model will be compared with some of the traditional machine learning models. Compared with the traditional AdaBoost, back propagation neural network (BPNN), regression classifier, support vector machine (SVM) and support vector regression (SVR), the proposed method possesses the better classification ability (average correct rate of the proposed method is 88.04%) in handling two classification problems. Compared with traditional AdaBoost, one-against-all SVM, one-against-one SVM, one-against-all SVR and one-against-one SVR, the classification ability of the proposed method is also relatively better for coping with the multi-class classification problem. Finally, some conclusions and future research will be discussed at the end.

scholarly journals Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jaweria Kainat ◽  
Syed Sajid Ullah ◽  
Fahd S. Alharithi ◽  
Roobaea Alroobaea ◽  
Saddam Hussain ◽  
...  
Keyword(s):  
Image Processing ◽  
Feature Fusion ◽  
Principal Component ◽  
Features Extraction ◽  
Support Vector ◽  
Features Fusion ◽  
Extraction Step ◽  
Processing Techniques ◽  
Feature Score

Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.

Facial Expression Recognition Based on Features Fusion

2010 ◽  
Vol 20-23 ◽  
pp. 1253-1259
Author(s):  
Chang Jun Zhou ◽  
Xiao Peng Wei ◽  
Qiang Zhang
Keyword(s):  
Principal Component ◽  
Local Features ◽  
Support Vector ◽  
Expression Recognition ◽  
Global Features ◽  
Features Fusion ◽  
Face Expression ◽  
The Face ◽  
Value Decomposition ◽  
Global And Local

In this paper, we propose a novel algorithm for facial recognition based on features fusion in support vector machine (SVM). First, some local features and global features from pre-processed face images are obtained. The global features are obtained by making use of singular value decomposition (SVD). At the same time, the local features are obtained by utilizing principal component analysis (PCA) to extract the principal Gabor features. Finally, the feature vectors which are fused with global and local features are used to train SVM to realize the face expression recognition, and the computer simulation illustrates the effectivity of this method on the JAFFE database.

An Image Classification Algorithm Based on SVM

2015 ◽  
Vol 738-739 ◽  
pp. 542-545 ◽  
Author(s):  
Chun Hua Qian ◽  
He Qun Qiang ◽  
Sheng Rong Gong
Keyword(s):  
Image Classification ◽  
Principal Component ◽  
Classification Algorithm ◽  
Gabor Wavelet ◽  
Image Feature ◽  
Support Vector ◽  
Risk Minimization ◽  
Method Base ◽  
Structural Risk ◽  
Gabor Wavelet Transformation

Image classification is a image processing method which to distinguish between different categories of objectives according to the different features of images. It is widely used in pattern recognition and computer vision. Support Vector Machine (SVM) is a new machine learning method base on statistical learning theory, it has a rigorous mathematical foundation, builts on the structural risk minimization criterion. We design an image classification algorithm based on SVM in this paper, use Gabor wavelet transformation to extract the image feature, use Principal Component Analysis (PCA) to reduce the dimension of feature matrix. We use orange images and LIBSVM software package in our experiments, select RBF as kernel function. The experimetal results demonstrate that the classification accuracy rate of our algorithm beyond 95%.

scholarly journals Application of ECOC SVMS in Remote Sensing Image Classification

2014 ◽  
Vol XL-2 ◽  
pp. 191-196 ◽  
Author(s):  
Z. Yan ◽  
Y. Yang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Small Sample ◽  
Support Vector ◽  
Classification Problems ◽  
Remote Sensing Image Classification ◽  
Gis Data ◽  
Multi Class Classification ◽  
Small Sample Sizes

Image processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only solve two-class problems while multi-class decision is impossible. Error correcting output coding (ECOC) SVMs enhance the ability of fault tolerance when solving multi-class classification problems, which makes ECOC SVMs suitable for remote sensing image classification. In this paper, the generalization ability of ECOC SVMs is discussed. ECOC SVMs with optimum coding matrices are selected by experiment, and applied to remote sensing image classification. Experimental results show that, compared with Conventional multi-class classification methods, less SVM sub-classifiers are needed for ECOC SVMs in remote sensing image classification, and the classification accuracy is also improved.

scholarly journals Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Yuan Shieh ◽  
Juing-Shian Chiou ◽  
Yu-Chia Hu ◽  
Kuo-Yang Wang
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Real Time ◽  
Selection Process ◽  
Principal Component ◽  
Facial Feature ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Classification Problems ◽  
Recognition Systems

This paper incorporates principal component analysis (PCA) with support vector machine-particle swarm optimization (SVM-PSO) for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

scholarly journals Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence

2019 ◽  
Vol 19 (S6) ◽  
Author(s):  
Shuang Song ◽  
Chenbing Du ◽  
Ying Chen ◽  
Danni Ai ◽  
Hong Song ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Principal Component ◽  
Image Sequence ◽  
Local Contrast ◽  
X Ray ◽  
Vascular Segmentation ◽  
Vascular Structures ◽  
Angiographic Image ◽  
Global And Local ◽  
Inter Frame

Abstract Background Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures. Methods A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method. Results Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence. Conclusions The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.

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