UTHM Institutional Repository

A novel image classification algorithm using swarm-based technique for image database

Wahid, Noorhaniza and Yuk, Ying Chung A novel image classification algorithm using swarm-based technique for image database. In: Ubiquitous Computing and Multimedia Applications, Communications in Computer and Information Science. Springer Berlin Heidelberg, pp. 460-470.

Full text not available from this repository.


Image data has become one of the most popular data type distributed in many multimedia applications. The effectiveness of image deployment is greatly dependent on the ability to classify and retrieve the image files based on their properties or content. However, image classification has faced a problem where the number of possible different combination of variables is very high. The algorithms which based on exhaustive search are unable to cope with the problem as the computational ability become infeasible. In this paper, a new image classification algorithm namely Simplified Swarm Optimization (SSO) has been proposed. This new approach is capable to obtain the high quality potential solution in the population which contributes to the improvement of the classification performance. This algorithm has been tested using image dataset which consists of seven classes of outdoor images. Moreover, the performance of SSO, Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) have been compared and analysed. The testing results show that SSO is more competitive than PSO and SVM, and can be fruitfully exploited in image database and solving image classification problem.

Item Type: Book Section
Uncontrolled Keywords: SSO; PSO; SVM; image classification
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 31 Jan 2013 07:40
Last Modified: 31 Jan 2013 07:40
URI: http://eprints.uthm.edu.my/id/eprint/2951
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item