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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Canadian Center of Science and Education
2011
|
| Subjects: | |
| Online Access: | http://dx.doi.org/10.5539/mas.v5n5p150 http://dx.doi.org/10.5539/mas.v5n5p150 http://eprints.uthm.edu.my/9624/1/J5053_83a4cde7c174ebbbb47d99a2128b52bb.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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. |
|---|