Ant Colony Optimization for Use in Content Based Image Retrieval

2011 ◽  
pp. 384-404
Author(s):  
Konstantinos Konstantinidis ◽  
Georgios Ch. Sirakoulis ◽  
Ioannis Andreadis
Keyword(s):  
Image Retrieval ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Spatial Information ◽  
Ant Colony ◽  
Content Based Image Retrieval ◽  
Computational Burden ◽  
Whole Process ◽  
Histogram Intersection ◽  
Speed Up

The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony optimization and fuzzy logic. This method utilizes a two-stage fuzzy modified ant colony algorithm employing in parallel low-level features such as color, texture and spatial information which are extracted from the images themselves. The results prove the system to be more efficient compared to popular and contemporary methods such as the histogram intersection, joint histograms and the scalable color histogram of the MPEG-7 standard. However, due to the high computational burden of the AI methods the system is quite slow when implemented in software. Thus in order to speed up the whole process the reader is also provided with the hardware implementation analysis of the whole system. The increase in speed is phenomenal.

scholarly journals A Performance Evaluation of Shape Based Image Retrieval Using Heuristic Function

2013 ◽  
Vol 12 (2) ◽  
pp. 3241-3248
Author(s):  
Parmalik Kumar ◽  
Pushpa Tandekar ◽  
Dhirendra Kumar Jha
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Image Retrieval ◽  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Ant Colony ◽  
Content Based Image Retrieval ◽  
Research Trend ◽  
Shape Feature ◽  
Heuristic Function

Heuristic function plays an important role in content based image retrieval. The heuristic function used for feature selection and feature optimization for retrieval process. The feature selection process are depends on feature extraction process. The content based image consists of three types of features such as color, texture and shape. The shape feature is very important feature for image retrieval. The extraction of shape feature various authors used different method such ad Gabor filter, wavelet transform function and Fourier descriptor. Now in current research trend MPEG-7 feature descriptor are mostly authors are used. In this paper descried the review of content based image retrieval based on shape based feature and optimization technique such as ANT colony optimization, genetic algorithm and neural network. The empirical evaluation result shows that ANT colony optimization technique is better optimization technique in compared with other such as genetic and neural network.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

Application of Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1217-1220
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pheromone Concentration ◽  
Local Optimal Solution ◽  
Searching Ability ◽  
Excessive Accumulation

This paper mainly considers the application of the ant colony in our life. The principle of ant colony optimization, improves the performance of ant colony algorithm, and the global searching ability of the algorithm. We introduce a new adaptive factor in order to avoid falling into local optimal solution. With the increase the number of interations, this factor will benefit the ant search the edge with lower pheromone concentration and avoid the excessive accumulation of pheromone.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

A flexibility method for structural damage identification using continuous ant colony optimization

2015 ◽  
Vol 11 (2) ◽  
pp. 186-201 ◽  
Author(s):  
Maryam Daei ◽  
S. Hamid Mirmohammadi
Keyword(s):  
Experimental Data ◽  
Damage Detection ◽  
Ant Colony Optimization ◽  
Structural Damage ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Computational Time ◽  
Content Type ◽  
Flexibility Matrix

Purpose – The interest in the ability to detect damage at the earliest possible stage is pervasive throughout the civil engineering over the last two decades. In general, the experimental techniques for damage detection are expensive and require that the vicinity of the damage is known and readily accessible; therefore several methods intend to detect damage based on numerical model and by means of minimum experimental data about dynamic properties or response of damaged structures. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the damage detection problem is formulated as an optimization problem such as to obtain the minimum difference between the numerical and experimental variables, and then a modified ant colony optimization (ACO) algorithm is proposed for solving this optimization problem. In the proposed algorithm, the structural damage is detected by using dynamically measured flexibility matrix, since the flexibility matrix of the structure can be estimated from only the first few modes. The continuous version of ACO is employed as a probabilistic technique for solving this computational problem. Findings – Compared to classical methods, one of the main strengths of this meta-heuristic method is the generally better robustness in achieving global optimum. The efficiency of the proposed algorithm is illustrated by numerical examples. The proposed method enables the deduction of the extent and location of structural damage, while using short computational time and resulting good accuracy. Originality/value – Finding accurate results by means of minimum experimental data, while using short computational time is the final goal of all researches in the structural damage detection methods. In this paper, it gains by applying flexibility matrix in the definition of objective function, and also via using continuous ant colony algorithm as a powerful meta-heuristic techniques in the constrained nonlinear optimization problem.

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