A Hybrid Bat‐Genetic Algorithm–Based Novel Optimal Wavelet Filter for Compression of Image Data

2021 ◽  
pp. 89-136
Author(s):  
Renjith V. Ravi ◽  
Kamalraj Subramaniam
Keyword(s):  
Genetic Algorithm ◽  
Image Data ◽  
Wavelet Filter

Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

2016 ◽  
Vol 2 (3) ◽  
pp. 703 ◽  
Author(s):  
B. Saichandana ◽  
K. Srinivas ◽  
R. KiranKumar
Keyword(s):  
Genetic Algorithm ◽  
Image Fusion ◽  
Image Classification ◽  
Empirical Mode Decomposition ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Hyperspectral Image Classification ◽  
Mode Decomposition ◽  
Fused Image

<p>Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. This paper presents hyperspectral image classification mechanism using genetic algorithm with empirical mode decomposition and image fusion used in preprocessing stage. 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, image fusion is performed on the hyperspectral bands to selectively merge the maximum possible features from the source images to form a single image. This fused image is classified using genetic algorithm. Different indices, such as K-means (KMI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) are used as objective functions. This method increases classification accuracy of hyperspectral image.</p>

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

2016 ◽  
Vol 3 (3) ◽  
pp. 503 ◽  
Author(s):  
R. Kiran Kumar ◽  
B. Saichandana ◽  
K. Srinivas
Keyword(s):  
Genetic Algorithm ◽  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Image Data ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Data Set ◽  
Mode Decomposition ◽  
Fused Image

<p>This paper presents genetic algorithm based band selection and classification on hyperspectral image data set. Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. In this paper, first filtering based on 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, band selection is done using genetic algorithm in-order to remove bands that convey less information. This dimensionality reduction minimizes many requirements such as storage space, computational load, communication bandwidth etc which is imposed on the unsupervised classification algorithms. Next image fusion is performed on the selected hyperspectral bands to selectively merge the maximum possible features from the selected images to form a single image. This fused image is classified using genetic algorithm. Three different indices, such as K-means Index (KMI) and Jm measure are used as objective functions. This method increases classification accuracy and performance of hyperspectral image than without dimensionality reduction.</p>

scholarly journals Multi-Objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection using Medical Image Data

2021 ◽  
Author(s):  
Shrey Bansal ◽  
Mukul Singh ◽  
Rahul Dubey ◽  
Bijaya Ketan Panigrahi
Keyword(s):  
Genetic Algorithm ◽  
Transmission Rate ◽  
Binary Classification ◽  
Feature Learning ◽  
Image Data ◽  
Support Vector ◽  
Current Standard ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Novel Coronavirus

Abstract In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19. In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT Scans. The model proposed in this study uses a three-step architecture, consisting of a Convolutional Autoencoder based unsupervised feature extractor, a Multi-Objective Genetic Algorithm based feature selector, and a Bagging Ensemble of Support Vector Machines(SVMs) based classifier. The Autoencoder generates a diverse set of features from the images, and an optimal subset, free of redundant and irrelevant features, is selected by the evolutionary selector. The Ensemble of SVMs then performs the binary classification of the features. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID vs.nonCOVID). A Dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model. The experimental results prove the superiority of the proposed methodology in comparison to existing methods.

scholarly journals An Evolved Wavelet Library Based on Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
D. Vaithiyanathan ◽  
R. Seshasayanan ◽  
K. Kunaraj ◽  
J. Keerthiga
Keyword(s):  
Genetic Algorithm ◽  
Image Compression ◽  
Signal To Noise Ratio ◽  
Local Maximum ◽  
Image Resolution ◽  
Wavelet Filter ◽  
Bandwidth Limitation ◽  
Local Filters ◽  
Global And Local ◽  
Considerable Loss

As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing thepeak signal to noise ratio(PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31 dB improvement in the average PSNR and a 0.39 dB improvement in the maximum PSNR.

scholarly journals Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 3892-3895
Author(s):  
B. K. Alsaidi ◽  
B. J. Al-Khafaji ◽  
S. A. A. Wahab
Keyword(s):  
Genetic Algorithm ◽  
Standard Deviation ◽  
Image Data ◽  
One Dimension ◽  
Image Clustering ◽  
Visual Features ◽  
Statistical Features ◽  
Textual Information ◽  
Clustering Technique ◽  
Web Images

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering. The extraction of features gave a high distinguishability and helped GA reach the solution more accurately and faster.

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