원문정보
초록
영어
With the fast development of data analysis and computer science technology, the design and implementation of image retrieval system has been a hot topic. The prior research focus more on image-size based approaches which are not intelligent or convenient. In this paper, we present a novel modified evolutionary algorithm based image retrieval framework theoretically with applications. To achieve more accuracy in less number of iteration, this paper, proposed a new approach to enhance the performance of content guided retrieval methodology by improving the performance of RF through Particle Swarm Optimization, Genetic Algorithm and Support Vector Machine. The objective of using Genetic Algorithm and Particle Swarm Optimization is to increase the number of images in relevant set where SVM is used to classify the relevant and irrelevant images. The experimental and numerical simulation indicate the efficiency of our method which means the presented technique is helpful in the fields where high accuracy rate of image retrieval is required. Further work of interest is also discussed in the final section.
목차
1. Introduction
2. The Background and Related Preliminary Knowledge
2.1. The Related Work
2.2. The Genetic Algorithm
2.3. The Particle Swarm Optimization Framework
3. Our Proposed Framework
3.1. Theoretical Overview
3.2. The Swarm Representation and User Feedback
3.3. The Swarm Initialization and Fitness Evaluation
3.4. The GA Procedure and Further Steps
4. Experiment and Simulation Result
4.1. The Set-up of the Experiment
4.2. The Experiment and Result
4.3. The Numeric Analysis of the Result
5. Conclusion and Summary
References