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Interactive Evolutionary Computation
Review of Applications
Praminda Caleb-Solly
Intelligent Computer Systems Centre
University of the West of England
Summary of Talk
• Application Areas– Motivation– Implementation– Salient Features
• Problematic Issues of IEC
Hearing Aid FittingH. Takagi and M. Osaki
• Motivation– personalisation of hearing aid compensation
characteristics in different acoustic environments
• Implementation
Hearing Aid Fitting• Implementation
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.
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
Image Retrieval• Implementation
Image Retrieval
Image Retrieval
• Salient Aspects– Correspondence between psychological
space and feature space
– Evaluation of retrieval performance
– Evaluation of features describing content
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.
Image Enhancement
• Salient Aspects– Limited user interaction
– Modelling the user
– Evolutionary algorithms transformed from inefficient search procedures into powerful and efficient search methods.
Problematic Issues of IEC
• User Fatigue
• Limited population
• Limited generations
• Convergence issues
• Robustness issues
• Evaluating Performance
Adaptive Image Segmentation Based on Interactive Evolutionary
Search
Praminda Caleb-Solly
Intelligent Computer Systems Centre
University of the West of England
Summary of Talk
• Description of Application Area
• Image Processing Technique
• Interactive Evolution
• Description of Implementation
• Evolutionary Algorithm
• Results
• Discussion of Research Issues
Hot Rolled Steel Surface Inspection
Components of the Decision Support System
Segmentation
Feature Extraction
Classification
Image Capture
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
Original Image Texture Image
Thresholded ImageSegmented Image
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
Interactive Evolution
• Three methods for manual intervention by the user
– Subjective Selection (Dawkins - Biomorphs)
– Subjective Problem Definition (Parmee - Evolutionary Design Systems)
– Subjective Evaluation
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.
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
VIFL Interface Tool
Results
Bronze Parameter Set Interactively Discovered Set
More Previously Unseen Images
Bronze Parameter Set Interactively Discovered Set
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