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.
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BRAC University
2016
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| 在线阅读: | http://hdl.handle.net/10361/5308 |
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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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