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