scholarly journals Image Edge Feature Extraction and Refining Based on Genetic-Ant Colony Algorithm

2015 ◽  
Vol 13 (1) ◽  
pp. 118
Author(s):  
Xing Zhang ◽  
Shuai Liu
Keyword(s):  
Feature Extraction ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Image Edge ◽  
Edge Feature

An Improved Image Edge Feature Extraction Algorithm Based on Ant Colony Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 120-123 ◽  
Author(s):  
Lin Gui
Keyword(s):  
Feature Extraction ◽  
Optimization Design ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
New Method ◽  
Advisory Council ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Image Edge ◽  
Edge Feature

In this paper, a new method for the optimization design of ant colony algorithm is used to extract the edge character of the model in the air of the image. The organization is as follows: the second part is a basic ant colony algorithm in the edge of the model feature extraction. The result of the advisory council on AIDS will be compared with the results analysis, and think this is smart operator most edge feature extraction operator. Contrast indicates that the algorithm can effectively edge feature extraction, especially the image.

A Kind of Network Intrusion Detection Method Using Improved Support Vector Machine Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2995-2998
Author(s):  
Xiaoqin Zhang ◽  
Guo Jun Jia
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Ant Colony Algorithm ◽  
Small Sample ◽  
Ant Colony ◽  
Support Vector ◽  
Algorithm Selection ◽  
Data Set ◽  
Network Intrusion

Support vector machine (SVM) is suitable for the classification problem which is of small sample, nonlinear, high dimension. SVM in data preprocessing phase, often use genetic algorithm for feature extraction, although it can improve the accuracy of classification. But in feature extraction stage the weak directivity of genetic algorithm impact the time and accuracy of the classification. The ant colony algorithm is used in genetic algorithm selection stage, which is better for the data pretreatment, so as to improve the classification speed and accuracy. The experiment in the KDD99 data set shows that this method is feasible.

scholarly journals An Image Edge Detection Method Based on Improved Ant Colony Algorithm

2011 ◽  
Vol 1 ◽  
pp. 236-240
Author(s):  
Xi Yun Wang ◽  
Pan Feng Huang ◽  
Ying Pings Fan
Keyword(s):  
Edge Detection ◽  
Ant Colony Algorithm ◽  
Detection Method ◽  
Ant Colony ◽  
Improved Method ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method ◽  
Background Image ◽  
Improved Ant Colony Algorithm

This paper raises an improved ant colony algorithm, for the detection of weak edge of complex background image, considering edge positioning accuracy, edge pixels, edge continuity and interference edges. This algorithm is improved in two aspects: first, we improved the expression of pheromone; second, we improved the calculation of Heuristic information. Compared with traditional Canny detector indicates, the improved method is proved to be accurate in edge detection, good continuity and less interference by experiment.

A New Medical Image Edge Detection Algorithm Based on BC-ACO

2017 ◽  
Vol 31 (03) ◽  
pp. 1757002
Author(s):  
Jingyu Zhang ◽  
Jianfu Teng ◽  
Yu Bai
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Ant Colony Algorithm ◽  
Bacterial Chemotaxis ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Ant Colony ◽  
Image Edge Detection ◽  
Image Edge ◽  
Improved Ant Colony Algorithm

Taking the improved ant colony algorithm based on bacterial chemotaxis as a means, this paper proposes one new swarm intelligence optimization algorithm to solve the medical image edge detection problem. The improved ant colony algorithm based on bacterial chemotaxis mainly aims at the shortcoming that the basic ant colony algorithm lacks initial pheromone, and combines bacterial chemotaxis algorithm with basic ant colony algorithm. Firstly, feasible better solution can be found through bacterial chemotaxis algorithm and fed back as initial pheromone. Then ant colony algorithm is implemented to search for the global optimal solution. The algorithm test indicates that the improved ant colony algorithm is more effective in the aspects of searching precision, reliability, optimization speed and stability compared with basic ant colony algorithm. Finally, the improved ant colony algorithm is applied into the edge detection of medical image. It can be seen from the computer simulation that compared with other operators and basic ant colony algorithm on the issue of solving medical image edge detection, the improved ant colony algorithm has superiority and the detected edge is clearer.

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