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Image classification technique using modified particle swarm optimization

Mohd Syukran, Mohd Afizi and Yuk, Ying Chung and Yeh, Wei-Chang and Wahid, Noorhaniza and Ahmad Zaidi , Ahmad Mujahid (2011) Image classification technique using modified particle swarm optimization. Modern Applied Science, 5 (5). pp. 150-164. ISSN 19131852

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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 proposed image classification using modified PSO classifier will be described in detail.

Item Type: Article
Uncontrolled Keywords: artificial bee colony algorithm; data mining; image classification
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Multimedia
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 21 Jan 2018 07:29
Last Modified: 21 Jan 2018 07:29
URI: http://eprints.uthm.edu.my/id/eprint/9624
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