interactive evolutionary computation review of applications praminda caleb-solly intelligent...
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Interactive Evolutionary Computation
Review of Applications
Praminda Caleb-Solly
Intelligent Computer Systems Centre
University of the West of England
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Summary of Talk
• Application Areas– Motivation– Implementation– Salient Features
• Problematic Issues of IEC
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Hearing Aid FittingH. Takagi and M. Osaki
• Motivation– personalisation of hearing aid compensation
characteristics in different acoustic environments
• Implementation
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Hearing Aid Fitting• Implementation
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Hearing Aid Fitting• Salient Aspects– Redefinition of filter characteristics based on
Gaussian Functions.– Subjective Evaluation of 20 individuals
graded on a 5 level scale. – psychological tests to compare clarity and
quality using processed sounds from IEC fitting, conventional loudness compensation and unprocessed original sounds.
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Image Retrieval1. H. Takagi, S.B.Cho and S. Noda
2. J.Y.Lee and S.B. Cho3. F. Boschetti and S.B.Cho
• Motivation– Enable retrieval of images based on content
rather than descriptive keywords• allowing incorporation of human preference and
emotion
• search for a specific feature inside an image
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Image Retrieval• Implementation
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Image Retrieval
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Image Retrieval
• Salient Aspects– Correspondence between psychological
space and feature space
– Evaluation of retrieval performance
– Evaluation of features describing content
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Image EnhancementR. Poli and S. Cagnoni
• Motivation– Expertise and knowledge of user required to
determine significant regions of interest in images.
• Implementation– Enhancement of MRI Images– Each program in the population is a solution
for altering pixels in the input images to obtain an output image
– User drives GP by deciding which individual should be the winner in tournament selection.
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Image Enhancement
• Salient Aspects– Limited user interaction
– Modelling the user
– Evolutionary algorithms transformed from inefficient search procedures into powerful and efficient search methods.
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Problematic Issues of IEC
• User Fatigue
• Limited population
• Limited generations
• Convergence issues
• Robustness issues
• Evaluating Performance
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Adaptive Image Segmentation Based on Interactive Evolutionary
Search
Praminda Caleb-Solly
Intelligent Computer Systems Centre
University of the West of England
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Summary of Talk
• Description of Application Area
• Image Processing Technique
• Interactive Evolution
• Description of Implementation
• Evolutionary Algorithm
• Results
• Discussion of Research Issues
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Hot Rolled Steel Surface Inspection
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Components of the Decision Support System
Segmentation
Feature Extraction
Classification
Image Capture
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Image SegmentationTexture Based Segmentation
• The Texture Measure • Kernel Dimensions• Step Size• Orientation Angle• Threshold
Normalise Image
Calculate Texture
Calculate Texture
Normalise and Median Filter
Normalise and Median Filter
Threshold and Pad
Threshold and Pad
OR Combined Image
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Original Image Texture Image
Thresholded ImageSegmented Image
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Standard Approaches
• Variety of classical search techniques such as adaptive thresholding and gradient descent used to develop “bronze” standard set.
• Process is time and knowledge intensive
• Not practical for real-time industrial use
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Interactive Evolution
• Three methods for manual intervention by the user
– Subjective Selection (Dawkins - Biomorphs)
– Subjective Problem Definition (Parmee - Evolutionary Design Systems)
– Subjective Evaluation
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Description of Implementation
8 IP parameter sets generated at random
Parent is the highest scoring individual. 8 offspring produced based on fitness score of parent.
User selects new image to score
User shown a set of 8 segmented images derived using
each of the parameter sets. Images from training set.
Calculate aggregate score for each of the 8 parameter sets
Best Score > Target ScoreYes
User sets target score
User scores each segmented image on a scale of 0 to 10
Write Results to Log file - Final Parameter sets and corresponding scores.
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Evolutionary Strategy
• (μ,λ) Strategy - (1,8)
• For Threshold Variables– Mutation Step size depends on the parents
fitness
• For Texture Measures– Depending on the parents fitness the parents
texture measure is retained in 50% of the offspring
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VIFL Interface Tool
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Results
Bronze Parameter Set Interactively Discovered Set
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More Previously Unseen Images
Bronze Parameter Set Interactively Discovered Set
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Research Issues
• Exploration of alternative strategies for choice of images
• Order of presentation of images
• Combination of interactive and normal EC
• Scoring strategy
• Algorithms for fast convergence