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

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dc.contributor.advisor Uddin, Jia
dc.contributor.author Hasan, Md. Junayed
dc.contributor.author Khan, Nazmul Kabir
dc.contributor.author Hridi, Navila Alam
dc.contributor.author Ontora, Fariha Tahsin
dc.date.accessioned 2016-05-22T13:43:54Z
dc.date.available 2016-05-22T13:43:54Z
dc.date.copyright 2016
dc.date.issued 2016-04
dc.identifier.other ID 12101050
dc.identifier.other ID 12101078
dc.identifier.other ID 12101080
dc.identifier.other ID 12101065
dc.identifier.uri http://hdl.handle.net/10361/5308
dc.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. en_US
dc.description Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (page 34-36).
dc.description.abstract 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. en_US
dc.description.statementofresponsibility Md. Junayed Hasan
dc.description.statementofresponsibility Nazmul Kabir Khan
dc.description.statementofresponsibility Navila Alam Hridi
dc.description.statementofresponsibility Fariha Tahsin Ontora
dc.format.extent 36 pages
dc.language.iso en en_US
dc.publisher BRAC University en_US
dc.rights 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.
dc.subject Computer science and engineering en_US
dc.subject CSE en_US
dc.subject HGAPSO en_US
dc.subject GLCO based SFTA en_US
dc.title A novel parallel feature extraction method using HGAPSO and GLCO based SFTA en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University
dc.description.degree B. Computer Science and Engineering


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