A novel image classification algorithm using swarm-based technique for image database
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 prob...
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| Main Authors: | , |
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| Format: | Book Section |
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Springer Berlin Heidelberg
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| Online Access: | http://eprints.uthm.edu.my/2951/ |
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| Summary: | 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. |
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