SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University

1. 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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2. 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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3. 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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4. Introduction
 Need for Arabian Horse Identification
 Biosecurity.
 Retrieval after theft.
 Fairness in competition.
 Medical record management.
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5. 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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6. Problem Statement
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Tattooing
Ear Tagging
Traditional Identification Methods
Branding
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7. Problem Statement
Using Biometric identifiers (contactless methods) for identification
and getting rid of harmful identification methods .
Muzzle Print IRIS
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8. Problem Statement
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9. 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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10. Phase I: Pre-processing
Pre-
Processing
Image
Resizing
Convert to
grayscale
Contrast
Stretching
Noise
Removal
Input Image Processed Image
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11. 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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12. 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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13. Phase II: Segmentation(continued)
Otsu-IFOA Segmentation
Binary MaskThresholded Image Segmented Area
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Processed Image
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14. 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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15. 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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16. Phase III :Feature Extraction
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(Gabor Filtering)
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17. Phase III :Feature Extraction
(Gabor Filtering)
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18. 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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19. 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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20. The Flowchart of DBPSO
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21. DBPSO Results
 We have 1800 extracted
features .
 265 selected features using
DBPSO.
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22. 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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23. 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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