A novel parallel feature extraction method using HGAPSO and GLCO based SFTA

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.

Detalhes bibliográficos
Principais autores:	Hasan, Md. Junayed, Khan, Nazmul Kabir, Hridi, Navila Alam, Ontora, Fariha Tahsin
Outros Autores:	Uddin, Jia
Formato:	Tese
Idioma:	English
Publicado em:	BRAC University 2016
Assuntos:	Computer science and engineering CSE HGAPSO GLCO based SFTA
Acesso em linha:	http://hdl.handle.net/10361/5308

id	10361-5308
record_format	dspace
spelling	10361-53082022-01-26T10:10:33Z A novel parallel feature extraction method using HGAPSO and GLCO based SFTA Hasan, Md. Junayed Khan, Nazmul Kabir Hridi, Navila Alam Ontora, Fariha Tahsin Uddin, Jia Department of Computer Science and Engineering, BRAC University Computer science and engineering CSE HGAPSO GLCO based SFTA This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. Cataloged from PDF version of thesis report. Includes bibliographical references (page 34-36). Content based visual information retrieval system (CBVIR) is an important system to know the information of the images. Image is much more powerful than a document because it can say a lot more than a document itself. Feature extraction is one of the major steps of CBVIR system. For image classification, there are bunch of methods.Segmentation Based Fractal Texture Analysis (SFTA) is an efficient texture feature method among them for its higher precision and accuracy. For large number of Dataset, it is necessary for optimizing the feature extraction time and accuracy. As a result, we bring a new approach on SFTA algorithm on our research. We use an optimum multilevel thresholding hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO with our proposed GLCO (Grey Level Classification Based Optimization) method for increasing the effectiveness of the SFTA technique. To avoid the computational complexity we have implemented our proposed HGAPSO based SFTA algorithm on NVIDIA Graphics Processing Unit (GPU).GeForce GTX 610 is fully utilized to perform the scanning to see its efficiency. Our experimental results show average 95.5% classification accuracy for our tested dataset and also the GPU based implementation experiences 120+ X speedup over conventional CPU implementation. Md. Junayed Hasan Nazmul Kabir Khan Navila Alam Hridi Fariha Tahsin Ontora B. Computer Science and Engineering 2016-05-22T13:43:54Z 2016-05-22T13:43:54Z 2016 2016-04 Thesis ID 12101050 ID 12101078 ID 12101080 ID 12101065 http://hdl.handle.net/10361/5308 en BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 36 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Computer science and engineering CSE HGAPSO GLCO based SFTA
spellingShingle	Computer science and engineering CSE HGAPSO GLCO based SFTA Hasan, Md. Junayed Khan, Nazmul Kabir Hridi, Navila Alam Ontora, Fariha Tahsin A novel parallel feature extraction method using HGAPSO and GLCO based SFTA
description	This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
author2	Uddin, Jia
author_facet	Uddin, Jia Hasan, Md. Junayed Khan, Nazmul Kabir Hridi, Navila Alam Ontora, Fariha Tahsin
format	Thesis
author	Hasan, Md. Junayed Khan, Nazmul Kabir Hridi, Navila Alam Ontora, Fariha Tahsin
author_sort	Hasan, Md. Junayed
title	A novel parallel feature extraction method using HGAPSO and GLCO based SFTA
title_short	A novel parallel feature extraction method using HGAPSO and GLCO based SFTA
title_full	A novel parallel feature extraction method using HGAPSO and GLCO based SFTA
title_fullStr	A novel parallel feature extraction method using HGAPSO and GLCO based SFTA
title_full_unstemmed	A novel parallel feature extraction method using HGAPSO and GLCO based SFTA
title_sort	novel parallel feature extraction method using hgapso and glco based sfta
publisher	BRAC University
publishDate	2016
url	http://hdl.handle.net/10361/5308
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A novel parallel feature extraction method using HGAPSO and GLCO based SFTA

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