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Metaheuristic based relevance feedback optimization with support vector machine in content based image retrieval

Imran, Muhammad (2015) Metaheuristic based relevance feedback optimization with support vector machine in content based image retrieval. PhD thesis, Universiti Tun Hussein Onn Malaysia.


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In this era of information technology, critical fields such as forensic and medical science generates large amount of images. This rapid increase in the digital contents (images) has made Content Based Image Retrieval (CBIR) an attractive research area in the domain of Multimedia. In conventional CBIR, low level features consisting of Color, Texture and Shape are used to search relevant images. However, these low level features are unable to search the similar images as per user semantics which is known as the gap between low level feature and user semantics Bridging this gap between the low level features and high level semantics, is one of the most important challenges for the CBIR. To solve this problem, Relevance Feedback (RF) coupled with Support Vector Machine (SVM) has been applied. However, when the size of positive samples marked by the user is small, the performance of CBIR is often unsatisfactory. To improve the performance of RF for CBIR, this thesis has proposed a new low level feature extraction technique named as CLD-cw and two new image retrieving techniques named as PSO-SVM-RF and PSOGA-SVM-RF, which combines RF and SVM with metaheuristic algorithms called Particle Swarm Optimization (PSO) and Genetic Algorithm. To prevent PSO from premature convergence, this thesis also proposed a Laplace mutated PSO. The aim of these new techniques is to minimize user interaction with the system by minimizing the number of RF. PSO-SVM-RF and PSOGA-SVM-RF were tested on coral photo gallery containing 10908 images. Precision, recall and F-Score were used to evaluate the proposed techniques. For the purpose of validation, the performance of developed approaches was compared with the performance of other well known CBIR techniques. This comparison was carried out based on precision and F-score. The experiments showed that PSO-SVMRF and PSOGA-SVM-RF achieved more than 30% accuracy in terms of precision than previous CBIR techniques. PSO-SVM-RF and PSOGA-SVM-RF also achieved higher value of precision and F-Score in less number of RF.

Item Type: Thesis (PhD)
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Depositing User: Normajihan Abd. Rahman
Date Deposited: 30 May 2016 07:01
Last Modified: 30 May 2016 07:01
URI: http://eprints.uthm.edu.my/id/eprint/8043
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