Application of PSO and K-means Clustering algorithm for CBIR

International Journal of Computer Trends and Technology (IJCTT)          
© 2019 by IJCTT Journal
Volume-67 Issue-5
Year of Publication : 2019
Authors : Aishwarya mohapatra, Subhashis Mishra, Gokulananda Das, Debashis Mishra, Utpal De
DOI :  10.14445/22312803/IJCTT-V67I5P124


MLA Style:Aishwarya mohapatra, Subhashis Mishra, Gokulananda Das, Debashis Mishra, Utpal De"Application of PSO and K-means Clustering algorithm for CBIR" International Journal of Computer Trends and Technology 67.5 (2019):141-145.

APA Style: Aishwarya mohapatra, Subhashis Mishra, Gokulananda Das, Debashis Mishra, Utpal De (2019) Application of PSO and K-means Clustering algorithm for CBIR International Journal of Computer Trends and Technology, 67(5), 141-145.

With the rapid increase in the digital data in various fields such as crime prevention, education etc, retrieval of data from the storage device has been an issue. It is due to comparison of the enquired images with of those present in the repository, using existing methods which leads to the rise in search space and algorithmic complexity. CBIR is one of the effective methods that calculate the similarity both of the images i.e. enquired images and stored images. In the following paper a hybrid technique of combine Particle Swarm Optimization (PSO) and K-clustering algorithms has been suggested for a CBIR method which performs on the basis of colour and texture of an image. Here four feature extraction methods are suggested to calculate the similarity: colour histogram, co-occurrence matrices, colour moment and wavelet moment. The final outcomes of the experiment have given a better accurate outcome compared to other systems.

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Content Based Image retrieval, CBIR, Image processing, Optimization, PSO, Clustering