Feature Selection using Dynamic Binary Particle Swarm...
Transcript of Feature Selection using Dynamic Binary Particle Swarm...
Feature Selection using Dynamic Binary Particle
Swarm Optimization for Arabian Horse
Identification System
Presented by: Samar Ibrahem Youssef
Annual Conference of the Institute of Studies and Statistical Research 2017
Workshop in Intelligent Systems and Applications
MSc Student , Information Technology Department,
Faculty of Computers and Information,
Cairo University
Scientific Research Group in Egypt
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Introduction
Need for Arabian Horse Identification
Biosecurity.
Retrieval after theft.
Fairness in competition.
Medical record management.
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Problem Statement
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Tattooing
Ear Tagging
Traditional Identification Methods
Branding
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Problem Statement
Using Biometric identifiers (contactless methods) for identification
and getting rid of harmful identification methods .
Muzzle Print IRIS
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Problem Statement
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Phase I: Pre-processing
Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Input Image Processed Image
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Phase II : Segmentation
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Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Segmentation
Otsu-IFOA
Opening & Masking
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Phase II: Segmentation(continued)
Otsu-IFOA Segmentation
Binary Mask Thresholded Image Segmented Area
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Processed Image
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Phase III: Feature Extraction
Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Segmentation
Otsu-FOA
Opening & Masking
Feature Extraction
Gabor Filtering
TDCT
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Phase III :Feature Extraction
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(Gabor Filtering)
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Phase III :Feature Extraction
(Gabor Filtering)
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Phase III: Feature Extraction(TDCT)
Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Segmentation
Otsu-FOA
Opening & Masking
Feature Extraction
Gabor Filtering
TDCT
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Phase IV: Feature Selection
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Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Segmentation
Otsu-FOA
Opening & Masking
Feature Extraction
Gabor Filter
TDCT
Feature Selection
DBPSO
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The Flowchart of DBPSO
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DBPSO Results
We have 1800 extracted
features .
265 selected features using
DBPSO.
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Conclusions & Future work
Automatic Periocular region segmentation using
Otsu-IFOA.
Feature Extraction(Gabor Filter + TDCT).
Feature Selection using DBPSO.
Selected Features can be used for Arabian Horse
Identification System.
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