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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    A novel parallel feature extraction method using HGAPSO and GLCO based SFTA

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    12101050.pdf (783.6Kb)
    Date
    2016-04
    Publisher
    BRAC University
    Author
    Hasan, Md. Junayed
    Khan, Nazmul Kabir
    Hridi, Navila Alam
    Ontora, Fariha Tahsin
    Metadata
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    URI
    http://hdl.handle.net/10361/5308
    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.
    Keywords
    Computer science and engineering; CSE; HGAPSO; 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.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 34-36).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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