Immune Programming Applications in Image Segmentation

Immune Programming Applications in Image Segmentation

Xiaojun Bi (Harbin Engineering University, P.R. China)
DOI: 10.4018/978-1-60566-310-4.ch014
OnDemand PDF Download:
$37.50

Abstract

In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete the task of image segmentation. Traditional evolutionary algorithm represented by Genetic Algorithm is an efficient approach for image segmentation, but in the practical application, there are many problems such as the slow convergence speed of evolutionary algorithm and premature convergence, which have greatly constrained the application. The goal of introducing immunity into the existing intelligent algorithms is to utilize some characteristics and knowledge in the pending problems for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Theoretical analysis and experimental results show that immune programming outperforms the existing optimization algorithms in global convergence speed and is conducive to alleviating the degeneration phenomenon. Theoretical analysis and experimental results show that immune programming has better global optimization and outperforms the existing optimization algorithms in alleviating the degeneration phenomenon. It is a feasible and effective method of image segmentation.
Chapter Preview
Top

Introduction

Based on the research of the characteristics mechanism of Artificial Immune System (AIS), the feasibility of applying Immune Programming (IP) into the digital image processing is further discussed. The IP mechanism is successfully used into image segmentation, and the image threshold segmentation algorithm based on the maximum information entropy theory and the image segmentation algorithm based on the expense function minimization are proposed in this paper, At the same time give out definite algorithm design and operation steps. We also do simulation experiment both on image threshold segmentation based on the maximum entropy theory and image segmentation based on cost function minimization, and compare them with classical genetic algorithm, analysis and summarize the segmentation result and experiment data. (Figure 1)

Figure 1.

The results of the maximum entropy threshold segmentation based on immune programming and quantification gray-level histogram

The structure of the chapter is organized as follows: Backgrounds and Previous Research Work provides a detailed review and a background of the existing methods of image segmentation, as well as the main challenges for these methods and the advantages of IP. The Main Thrust of the Chapter consists of two parts: firstly, the image threshold segmentation based on the maximum entropy theory is introduced, and the advantages and disadvantages of this method are demonstrated by several groups experiment; in the second part, the image segmentation based on cost function minimization is introduced, and simulation experiment results is given . Concluding Remarks concludes the whole chapter with the special emphasis on: The IP mechanism is successfully introduced into image segmentation, and the image threshold segmentation algorithm based on the maximum information entropy theory and the image segmentation algorithm based on the expense function minimization are proposed in this paper. This method is fit for not only the images with double-peak-shaped histogram, but also those of complicated-shaped histogram. finally, the future directions are addressed in Future Research Directions. The terms and definitions, as well as an additional reading list, can be found at the end of the chapter.

Top

Backgrounds And Previous Research Work

Image segmentation is a technical process which can divide an image into regions with certain and special characteristics and extract the objectives interested from them. Classic image segmentation is to construct a differential operator that is sensitive to pixel gray-level’s step changing, such as Laplace operator, Roberts gradient operator, Sobel gradient operator, etc.

The speed of edge detection based on operators is high, but the results obtained are always intermittent and incomplete information. And this kind of methods is sensitive to noises, so the influence of edge feature from noises is great. For the image with significant double peak and comparative deep valley-bottom histogram, the acceptable segmentation can be got with traditional evolutionary algorithm quickly. Contrarily for the image without such features, in the complex cases such as the target and background are multi or close gray levels, or the gray histogram is multi-peak or single peak but no main valley-bottom, traditional evolutionary algorithm is easy to get into the local optimum, and can not get acceptable segmentation.

Key Terms in this Chapter

Image Segmentation: Image segmentation is a technical process which can divide an image into regions with certain and special characteristics and extract the objectives interested from them. Classic image segmentation is to construct a differential operator that is sensitive to pixel gray-level’s step changing, such as Laplace operator, Roberts gradient operator, Sobel gradient operator, etc.

Antibody (Ab): Ab is the candidate solutions of the problem to be optimized.

Fitness (affinity): For conventional evolutionary algorithms, ranking and fitness assignment are only based on the information from the objective space.

Expense Function: The expense function of the segmentation image is consisted of the information of region distribution and edge distribution, it is defined as. . Where,is the gray variance of region, is the number of the segmentation regions, so the expense function of the region distribution is the summation of the gray variance of all the regions. And theis the expense function of the edge distribution. At each pixel, is defined as the weighted sums.

Antigen (Ag): Ag is the problem to be optimized.

Immune Programming: Immune Programming (IP) combining immune mechanism and evolution mechanism, is a novel idea of utilizing Artificial Immune System (AIS) into engineering application. As a global optimization algorithm with strong robustness, immune programming absorbs the advantage of genetic algorithm——parallel searching. It can construct immune operator by utilizing local characteristic information. By vaccinating and immune selecting, it can intervene the parallel global searching with certain intensity, and effectively restrain the degenerative phenomena in the existing evolution algorithms.

Diversity: For a population-based GA, diverity is maintianed through the mutation and an adequate population size; obviously, the population size is problem-contingent.

Maximum entropy: Digital image consists of pixels, in which the pixels that are different intensity belong to different regions. Sequentially, different shapes are displayed, while different shapes contains of different entropy. Therefore, image entropy can describe shape. For a image, assume that image intensity is nonnegative, that is , then we define image entropyas follow, ,where,. When an image has the equivalent probability of every intensity, the uncertainty of shape in image will reach its max, that is, the image contains the maximum entropy.

Complete Chapter List

Search this Book:
Reset
Editorial Advisory Board
Table of Contents
Foreword
Lipo Wang
Preface
Hongwei Mo
Chapter 1
Fabio Freschi, Carlos A. Coello Coello, Maurizio Repetto
This chapter aims to review the state of the art in algorithms of multiobjective optimization with artificial immune systems (MOAIS). As it will be... Sample PDF
Multiobjective Optimization and Artificial Immune Systems: A Review
$37.50
Chapter 2
Jun Chen, Mahdi Mahfouf
The primary objective of this chapter is to introduce Artificial Immune Systems (AIS) as a relatively new bio-inspired optimization technique and to... Sample PDF
Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications
$37.50
Chapter 3
Licheng Jiao, Maoguo Gong, Wenping Ma
Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection... Sample PDF
An Artificial Immune Dynamical System for Optimization
$37.50
Chapter 4
Malgorzata Lucinska, Slawomir T. Wierzchon
Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they... Sample PDF
An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
$37.50
Chapter 5
Luis Fernando Niño Vasquez, Fredy Fernando Muñoz Mopan, Camilo Eduardo Prieto Salazar, José Guillermo Guarnizo Marín
Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high... Sample PDF
Applications of Artificial Immune Systems in Agents
$37.50
Chapter 6
Xingquan Zuo
Inspired from the robust control principle, a robust scheduling method is proposed to solve uncertain scheduling problems. The uncertain scheduling... Sample PDF
An Immune Algorithm Based Robust Scheduling Methods
$37.50
Chapter 7
Fabio Freschi, Maurizio Repetto
The increasing cost of energy and the introduction of micro-generation facilities and the changes in energy production systems require new... Sample PDF
Artificial Immune System in the Management of Complex Small Scale Cogeneration Systems
$37.50
Chapter 8
Krzysztof Ciesielski, Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
In this chapter the authors discuss an application of an immune-based algorithm for extraction and visualization of clusters structure in large... Sample PDF
Applying the Immunological Network Concept to Clustering Document Collections
$37.50
Chapter 9
Xiangrong Zhang, Fang Liu
The problem of feature selection is fundamental in various tasks like classification, data mining, image processing, conceptual learning, and so on.... Sample PDF
Feature Selection Based on Clonal Selection Algorithm: Evaluation and Application
$37.50
Chapter 10
Yong-Sheng Ding, Xiang-Feng Zhang, Li-Hong Ren
Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network... Sample PDF
Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet
$37.50
Chapter 11
Tao Gong
Static Web immune system is an important applicatiion of artificial immune system, and it is also a good platform to develop new immune computing... Sample PDF
A Static Web Immune System and Its Robustness Analysis
$37.50
Chapter 12
Alexander O. Tarakanov
Based on mathematical models of immunocomputing, this chapter describes an approach to spatio-temporal forecast (STF) by intelligent signal... Sample PDF
Immunocomputing for Spatio-Temporal Forecast
$37.50
Chapter 13
Fu Dongmei
In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object... Sample PDF
Research of Immune Controllers
$37.50
Chapter 14
Xiaojun Bi
In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete... Sample PDF
Immune Programming Applications in Image Segmentation
$37.50
Chapter 15
Xin Wang, Wenjian Luo, Zhifang Li, Xufa Wang
A hardware immune system for the error detection of MC8051 IP core is designed in this chapter. The binary string to be detected by the hardware... Sample PDF
A Hardware Immune System for MC8051 IP Core
$37.50
Chapter 16
Mark Burgin, Eugene Eberbach
There are different models of evolutionary computations: genetic algorithms, genetic programming, etc. This chapter presents mathematical... Sample PDF
On Foundations of Evolutionary Computation: An Evolutionary Automata Approach
$37.50
Chapter 17
Terrence P. Fries
Path planning is an essential component in the control software for an autonomous mobile robot. Evolutionary strategies are employed to determine... Sample PDF
Evolutionary Path Planning for Robot Navigation Under Varying Terrain Conditions
$37.50
Chapter 18
Konstantinos Konstantinidis, Georgios Ch. Sirakoulis, Ioannis Andreadis
The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony... Sample PDF
Ant Colony Optimization for Use in Content Based Image Retrieval
$37.50
Chapter 19
Miroslav Bursa, Lenka Lhotska
The chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of medical data clustering. Clustering is a... Sample PDF
Ant Colonies and Data Mining
$37.50
Chapter 20
Bo-Suk Yang
This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global... Sample PDF
Artificial Life Optimization Algorithm and Applications
$37.50
Chapter 21
Martin Macaš, Lenka Lhotská
A novel binary optimization technique is introduced called Social Impact Theory based Optimizer (SITO), which is based on social psychology model of... Sample PDF
Optimizing Society: The Social Impact Theory Based Optimizer
$37.50
Chapter 22
James F. Peters, Shabnam Shahfar
The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for... Sample PDF
Ethology-Based Approximate Adaptive Learning: A Near Set Approach
$37.50
Chapter 23
Dingju Zhu
Parallel computing is more and more important for science and engineering, but it is not used so widely as serial computing. People are used to... Sample PDF
Nature Inspired Parallel Computing
$37.50
Chapter 24
Tang Mo, Wang Kejun, Zhang Jianmin, Zheng Liying
An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is... Sample PDF
Fuzzy Chaotic Neural Networks
$37.50
About the Contributors