A new approach for spectral feature extraction and for unsupervised classification of hyperspectral data based on the Gaussian mixture model

2001 ◽  
Vol 20 (2) ◽  
pp. 123-167 ◽  
Author(s):  
A. Koltunov ◽  
E. Ben‐Dor
Keyword(s):  
Feature Extraction ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Spectral Feature ◽  
Unsupervised Classification ◽  
Gaussian Mixture ◽  
Hyperspectral Data ◽  
New Approach

scholarly journals Detection and Classification of Vehicles on Curved Roads

2019 ◽  
Vol 8 (10S) ◽  
pp. 152-156
Keyword(s):  
Object Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Moving Object Detection ◽  
Morphological Operations ◽  
Moving Vehicles ◽  
Blob Analysis

Detection of a vehicle is a very important aspect for traffic monitoring. It is based on the concept of moving object detection. Classifying the detected object as vehicle and class of vehicle is also having application in various application domains. This paper aims at providing an application of vehicle detection and classification concept to detect vehicles along curved roads in Indian scenarios. The main purpose is to ensure safety in such roads. Gaussian mixture model and blob analysis are the methods applied for the detection of vehicles. Morphological operations are used to eliminate noise. The moving vehicles are detected and the class of the vehicle is identified.

Adaptive Gaussian Mixture Model and Convolution Autoencoder Clustering for Unsupervised Seismic Waveform Analysis

2021 ◽  
pp. 1-46
Author(s):  
Donglin Zhu ◽  
Jingbin Cui ◽  
Yan Li ◽  
Zhonghong Wan ◽  
Lei Li
Keyword(s):  
Feature Extraction ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Facies Analysis ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Waveform Analysis ◽  
Western China ◽  
Reservoir Properties ◽  
Seismic Waveform

Seismic facies analysis can effectively estimate reservoir properties and seismic waveform clustering is a useful tool for facies analysis. We developed a deep learning-based clustering approach called the modified deep convolutional embedded clustering with adaptive Gaussian mixture model (AGMM-MDCEC) for seismic waveform clustering. Trainable feature extraction and clustering layers in AGMM-MDCEC are implemented using neural networks. The two independent processes of feature extraction and clustering are fused, such that extracted features are modified simultaneously with the results of clustering. A convolutional autoencoder is used in the algorithm for extracting features from seismic data and reduce data redundancy. At the same time, weights of clustering network are fined-tuned through iteration to obtain state-of-the-art clustering results. We apply our new classification algorithm to a data volume acquired in western China to map architectural elements of a complex fluvial depositional system. Our proposed method obtains superior results over those provided by traditional K-means, Gaussian mixture model, and some machine learning methods, and improves the mapping of the extent of the distributary system.

Feature Selection Based on Gaussian Mixture Model Clustering for the Classification of Pulmonary Nodules Based on Computed Tomography

2020 ◽  
Vol 10 (5) ◽  
pp. 1033-1039
Author(s):  
Huihong Duan ◽  
Xu Wang ◽  
Xingyi He ◽  
Yonggang He ◽  
Litao Song ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Feature Selection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Pulmonary Nodules ◽  
Gaussian Mixture ◽  
Local Optimum ◽  
Computer Aided ◽  
Aided Diagnosis

Background: In the pulmonary nodules computer aided diagnosis systems (CAD), feature selection plays an important role in reducing the false positive rate and improving the system accuracy. To solve the problem of feature selection techniques by which the diversity of features was damaged in the process of distinguishing malignant pulmonary nodules from benign pulmonary nodules, this study developed a novel feature selection algorithm for improving the accuracy of traditional computer-aided differential diagnosis for benign and malignant classification of pulmonary nodules. Method: Firstly, we divided the extracted features of nodules into several groups by using Gaussian mixture model (GMM). Secondly, we applied Relief and sequential forward selection (SFS) algorithm to find local optimum features dataset for each group. Afterwards, we used the optimumpath forest (OPF) classifier with the found features dataset to obtain the classification results. Finally, the local optimum features dataset with the highest area under curve AUC in all groups were added into the final selected set. Results: According to collected pulmonary nodules on computed tomography (CT) scans, tested with two set of samples, we achieved an average accuracy of 89.5%, sensitivity of 87.1% and specificity of 90.9% on the first set of samples, and 90.1%, 88.7% and 92.1% on the second set of samples. The areas under the receiver operating characteristic (ROC) curves based on these two sample sets were 95.2%, and 96.3% respectively. Conclusions: This study shows that the proposed method was promising for improving the pulmonary nodules computer aided diagnosis systems performance of benign and malignant pulmonary nodules.

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