Somatic Cells Recognition by Application of Gabor Feature-Based (2D)2PCA

2017 ◽  
Vol 31 (12) ◽  
pp. 1757009 ◽  
Author(s):  
Xiaojing Gao ◽  
Heru Xue ◽  
Xin Pan ◽  
Xinhua Jiang ◽  
Yanqing Zhou ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Somatic Cells ◽  
Bovine Mastitis ◽  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Large Set ◽  
Novel Approach ◽  
Gabor Feature ◽  
Feature Based

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

Recognition of Somatic Cells in Bovine Milk Using Fusion Feature

2018 ◽  
Vol 32 (07) ◽  
pp. 1850021 ◽  
Author(s):  
Xiaojing gao ◽  
Heru Xue ◽  
Xinhua Jiang ◽  
Yanqing Zhou
Keyword(s):  
Cloud Model ◽  
Somatic Cells ◽  
Bovine Milk ◽  
Back Propagation ◽  
Bovine Mastitis ◽  
Support Vector ◽  
Large Set ◽  
Image Processing Algorithm ◽  
Different Sources ◽  
Fusion Feature

Mastitis is the major cause of loss in dairy farming. Somatic cells are one of most important standards to detect this infection. This paper proposes a novel image processing algorithm to recognize four types of somatic cells in bovine milk automatically. First, cloud model uses to segment cell images. Second, a variety of features are extracted from regions of interest. Finally, most differential features are selected using ReliefF algorithm and performances of two classifiers, Back propagation networks (BPN) and support vector machine (SVM), are compared. The experimental results are obtained using a large set of images from different sources. The results of our proposed method is not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

Efficient Feature Descriptor using Gabor Filter and Principal Component Analysis for Glaucoma Diagnosis

2021 ◽  
pp. 6787-6794
Author(s):  
Anisha Rebinth, Dr. S. Mohan Kumar
Keyword(s):  
Principal Component Analysis ◽  
Gabor Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Diagnosis ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Glaucoma Diagnosis

An automated Computer Aided Diagnosis (CAD) system for glaucoma diagnosis using fundus images is developed. The various glaucoma image classification schemes using the supervised and unsupervised learning approaches are reviewed. The research paper involves three stages of glaucoma disease diagnosis. First, the pre-processing stage the texture features of the fundus image is recorded with a two-dimensional Gabor filter at various sizes and orientations. The image features are generated using higher order statistical characteristics, and then Principal Component Analysis (PCA) is used to select and reduce the dimension of the image features. For the performance study, the Gabor filter based features are extracted from the RIM-ONE and HRF database images, and then Support Vector Machine (SVM) classifier is used for classification. Final stage utilizes the SVM classifier with the Radial Basis Function (RBF) kernel learning technique for the efficient classification of glaucoma disease with accuracy 90%.

Learning Target Class Feature Subspace (LTC-FS) Using Eigenspace Analysis and N-ary Search-Based Autonomous Hyperparameter Tuning for OCSVM

2021 ◽  
Author(s):  
Sanjay Kumar Sonbhadra ◽  
Sonali Agarwal ◽  
P. Nagabhushan
Keyword(s):  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Feature Subset ◽  
Target Class ◽  
Significant Information ◽  
Feature Extraction Method ◽  
Specificity And Sensitivity ◽  
Feature Subspace ◽  
Novel Target

Existing dimensionality reduction (DR) techniques such as principal component analysis (PCA) and its variants are not suitable for target class mining due to the negligence of unique statistical properties of class-of-interest (CoI) samples. Conventionally, these approaches utilize higher or lower eigenvalued principal components (PCs) for data transformation; but the higher eigenvalued PCs may split the target class, whereas lower eigenvalued PCs do not contribute significant information and wrong selection of PCs leads to performance degradation. Considering these facts, the present research offers a novel target class-guided feature extraction method. In this approach, initially, the eigendecomposition is performed on variance–covariance matrix of only the target class samples, where the higher- and lower-valued eigenvectors are rejected via statistical analysis, and the selected eigenvectors are utilized to extract the most promising feature subspace. The extracted feature-subset gives a more tighter description of the CoI with enhanced associativity among target class samples and ensures the strong separation from nontarget class samples. One-class support vector machine (OCSVM) is evaluated to validate the performance of learned features. To obtain optimized values of hyperparameters of OCSVM a novel [Formula: see text]-ary search-based autonomous method is also proposed. Exhaustive experiments with a wide variety of datasets are performed in feature-space (original and reduced) and eigenspace (obtained from original and reduced features) to validate the performance of the proposed approach in terms of accuracy, precision, specificity and sensitivity.

Hybrid Feature Vector-Assisted Action Representation for Human Action Recognition Using Support Vector Machines

2021 ◽  
pp. 1-22
Author(s):  
L. Nirmala Devi ◽  
A.Nageswar Rao
Keyword(s):  
Action Recognition ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Gabor Filter ◽  
Principal Component ◽  
Human Action Recognition ◽  
Human Action ◽  
Visual Surveillance ◽  
Support Vector ◽  
Significant Research

Human action recognition (HAR) is one of most significant research topics, and it has attracted the concentration of many researchers. Automatic HAR system is applied in several fields like visual surveillance, data retrieval, healthcare, etc. Based on this inspiration, in this chapter, the authors propose a new HAR model that considers an image as input and analyses and exposes the action present in it. Under the analysis phase, they implement two different feature extraction methods with the help of rotation invariant Gabor filter and edge adaptive wavelet filter. For every action image, a new vector called as composite feature vector is formulated and then subjected to dimensionality reduction through principal component analysis (PCA). Finally, the authors employ the most popular supervised machine learning algorithm (i.e., support vector machine [SVM]) for classification. Simulation is done over two standard datasets; they are KTH and Weizmann, and the performance is measured through an accuracy metric.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6429
Author(s):  
Lotfi Tlig ◽  
Moez Bouchouicha ◽  
Mohamed Tlig ◽  
Mounir Sayadi ◽  
Eric Moreau
Keyword(s):  
Forest Fire ◽  
Color Image ◽  
Gabor Filter ◽  
Human Life ◽  
Principal Component ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Full Account ◽  
Native Flora ◽  
Novel Approach

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance

2014 ◽  
Vol 15 (5) ◽  
pp. 1480-1500 ◽  
Author(s):  
Motong Qiao ◽  
Wei Wang ◽  
Michael Ng
Keyword(s):  
Gabor Filter ◽  
Error Function ◽  
Feature Space ◽  
Wasserstein Distance ◽  
Energy Functional ◽  
Phase Image ◽  
Minimization Method ◽  
Closed Form Solutions ◽  
Gabor Feature ◽  
Multi Phase

AbstractWe present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.

scholarly journals Hyperspectral image classification using support vector machines

2020 ◽  
Vol 9 (4) ◽  
pp. 684
Author(s):  
Jonnadula Dr.J.Harikiran Harikiran
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Bidimensional Empirical Mode Decomposition

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

scholarly journals A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters

2021 ◽  
Vol 13 (9) ◽  
pp. 239
Author(s):  
Danveer Rajpal ◽  
Akhil Ranjan Garg ◽  
Om Prakash Mahela ◽  
Hassan Haes Alhelou ◽  
Pierluigi Siano
Keyword(s):  
Character Recognition ◽  
Large Population ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Hybrid Features ◽  
Efficient Management ◽  
Novel Approach ◽  
Proposed Model ◽  
Machine Learning Approach

Hindi is the official language of India and used by a large population for several public services like postal, bank, judiciary, and public surveys. Efficient management of these services needs language-based automation. The proposed model addresses the problem of handwritten Hindi character recognition using a machine learning approach. The pre-trained DCNN models namely; InceptionV3-Net, VGG19-Net, and ResNet50 were used for the extraction of salient features from the characters’ images. A novel approach of fusion is adopted in the proposed work; the DCNN-based features are fused with the handcrafted features received from Bi-orthogonal discrete wavelet transform. The feature size was reduced by the Principal Component Analysis method. The hybrid features were examined with popular classifiers namely; Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). The recognition cost was reduced by 84.37%. The model achieved significant scores of precision, recall, and F1-measure—98.78%, 98.67%, and 98.69%—with overall recognition accuracy of 98.73%.

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