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Content based Structural Recognition for Image Classification using PSO Technique and SVM

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 7
Year of Publication: 2014
Abhishek Pandey
Anjna Jayant Deen
Rajeev Pandey

Abhishek Pandey, Anjna Jayant Deen and Rajeev Pandey. Article: Content based Structural Recognition for Image Classification using PSO Technique and SVM. International Journal of Computer Applications 87(7):6-11, February 2014. Full text available. BibTeX

	author = {Abhishek Pandey and Anjna Jayant Deen and Rajeev Pandey},
	title = {Article: Content based Structural Recognition for Image Classification using PSO Technique and SVM},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {87},
	number = {7},
	pages = {6-11},
	month = {February},
	note = {Full text available}


The issue of SVMs parameter optimization with particle swarm optimization (pso) provide the optimum solution. This new classification approach may be an efficient alternative, in existing paradigms. PSO technique work with high dimensional datasets and mixed attribute data. The structure of the image is recognized through PSO technique which provide optimized parameter for SVM. This approach determines the performance of image classification after structural recognition based on content of image and comparing the obtained results with those reported for various other classification approaches. PSO-SVM technique can be applied mixed-attribute, hyperspectral data, hyperdimension spaces & problem description spaces and it can also be a competitive alternative to well established classification techniques. The optimized process of data reduces the unclassified region of support vector machine and improves the performance of image classification. The feature of region of image is classified by PSO-SVM technique in inside the image. Cassified features are increase recogniztion ratio because the feature of image is optimized.


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