www.ccsenet.org/mas Modern Applied Science Vol. 5, No. 5; October 2011 ISSN 1913-1844 E-ISSN 1913-1852 150 Image Classification Technique using Modified Particle Swarm Optimization Mohd Afizi Mohd Shukran (Corresponding author) Department of Computer Science, Faculty of Science & Defense Technology National Defense University of Malaysia (NDUM), Sungai Besi Camp 57000 Kuala Lumpur, Malaysia Tel: +603 90513400 E-mail: afizi@upnm.edu.my Yuk Ying Chung School of Information Technologies, Faculty of Engineering & IT University of Sydney, 2006 NSW, Australia Tel: 61-2-9036-9109 E-mail: vchung@it.usyd.edu.au Wei-Chang Yeh Advanced Analytics Institute, Faculty of Engineering and Information Technology University of Technology Sydney, PO Box 123 Broadway, NSW 2007, Australia Tel: 61-2 9036-9109 E-mail: yeh@ieee.org Noorhaniza Wahid Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia 86400 Parit Raja, Batu Pahat Johor Tel: 60-7-453-8215 Email: nhaniza@uthm.edu.my Ahmad Mujahid Ahmad Zaidi Department of Mechanical Engineering, Faculty of Engineering National Defense University of Malaysia (NDUM), Sungai Besi Camp 57000 Kuala Lumpur, Malaysia Tel: 60-3 9051-3400 E-mail: mujahid@upnm.edu.my Received: July 2, 2011 Accepted: July 21, 2011 doi:10.5539/mas.v5n5p150 Abstract Image classification is becoming ever more important as the amount of available multimedia data increases. With the rapid growth in the number of images, there is an increasing demand for effective and efficient image indexing mechanisms. For large image databases, successful image indexing will greatly improve the efficiency of content based image classification. One attempt to solve the image indexing problem is using image classification to get high-level concepts. In such systems, an image is usually represented by various low-level features, and high-level concepts are learned from these features. PSO has recently attracted growing research interest due to its ability to learn with small samples and to optimize high-dimensional data. Therefore, this paper will introduce the related work on image feature extraction. Then, several techniques of image feature extraction will be introduced which include two main methods. These methods are RGB and Discrete Cosine Transformation (DCT). Finally, several experimental designs and results concerning the application of the