Image Classification Technique using Modified Particle Swarm Optimization

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 gre...

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Bibliographic Details
Published in:Modern applied science 2011-10, Vol.5 (5), p.150-150
Main Authors: Mohd Shukran, Mohd Afizi, Chung, Yuk Ying, Yeh, Wei-Chang, Wahid, Noorhaniza, Ahmad Zaidi, Ahmad Mujahid
Format: Article
Language:English
Subjects:
Design engineering
Feature extraction
Image classification
Indexing
Multimedia
Optimization
Transformations
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Summary: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.
ISSN:1913-1844
1913-1852
DOI:10.5539/mas.v5n5p150