Call for Paper - May 2014 Edition
IJCA solicits original research papers for the May 2014 Edition. Last date of manuscript submission is April 21, 2014. Read More

Cost Parameter Analysis and Comparison of Linear Kernel and Hellinger Kernel Mapping of SVM on Image Retrieval and Effects of Addition of Positive Images

Print
PDF
International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 73 - Number 2
Year of Publication: 2013
Authors:
Swathi Rao G
Anuj Sharma
10.5120/12711-9517

Swathi Rao G and Anuj Sharma. Article: Cost Parameter Analysis and Comparison of Linear Kernel and Hellinger Kernel Mapping of SVM on Image Retrieval and Effects of Addition of Positive Images. International Journal of Computer Applications 73(2):5-12, July 2013. Published by Foundation of Computer Science, New York, USA. BibTeX

@article{key:article,
	author = {Swathi Rao G and Anuj Sharma},
	title = {Article: Cost Parameter Analysis and Comparison of Linear Kernel and Hellinger Kernel Mapping of SVM on Image Retrieval and Effects of Addition of Positive Images},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {73},
	number = {2},
	pages = {5-12},
	month = {July},
	note = {Published by Foundation of Computer Science, New York, USA}
}

Abstract

In this paper we have brought out the analysis and comparison of cost parameter validation in Support vector machine using two different kernel mappings i. e. the linear and the Hellinger kernel. This paper also shows and discusses the results of the addition of positive images to the respective class of images with different cost parameters. The analysis is carried out using Matlab R2009a and C environment. The results obtained show that the increase in cost parameter for linear kernel gives much better results whereas for Hellinger kernel the performance decreases as cost parameter is increased. In the other hand, two classes of images are taken and they are tested by increasing the number of positive images gradually and the results show that the addition of positive class of images to a database can increase the performance of the system employed.

References

  • Vedaldi, A. , and Zisserman, A. Efficient Additive Kernels via Explicit Feature Maps. June 2011. IEEE transactions on pattern analysis and machine intelligence, vol. xx, no. xx,.
  • Berkeley, U. C. , Alexander C. , and Malik, J. Classification Using Intersection Kernel Support Vector Machines is Efficient.
  • Rojas, M. , Opido, I. D. , Plaza, A. , and Gamba. P. Comparison of Support Vector Machine-Based Processing Chains for Hyper spectral Image Classification. Satellite Data Compression, Communications, and Processing VI, edited by Bormin Huang, Antonio J. Plaza, Joan Serra-Sagristà, Chulhee Lee, Yunsong Li, Shen-En Qian, Proc. of SPIE Vol. 7810, 78100B.
  • Durgesh , k. , and lekha, B. Data Classification Using Support Vector Machine. Journal of Theoretical and Applied Information Technology.
  • Wang, Y. , and Gang Hu, B. January 2002. Hierarchical Image Classification Using Support Vector Machines. ACCV2002: The 5th Asian Conference on Computer Vision, Melbourne, Australia.
  • Juan C. C. , Cruz, A. , and Fabio A. G. Histopathology Image Classification using Bag of Features and Kernel Functions. Bioingenium Research Group National University of Colombia.
  • Claudio , C. , Ciocca, G. , and Schettini, R. Image annotation using SVM.
  • Gidudu , A. , Tshilidzi, M. , and Gregg, H. Image Classification Using SVMs One-against-One Vs One-against-All.
  • Xiaohong Yu and Hong Liu. "Image Semantic Classification Using SVM in Image Retrieval". ISBN 978-952-5726-07-7 (Print), 978-952-5726-08-4 (CD-ROM) Proceedings of the Second Symposium International Computer Science and Computational Technology(ISCSCT '09) Huangshan, P. R. China, 26-28,Dec. 2009, pp. 458-461.
  • Boughorbel, S. , Tarel, J. P. , and Boujemaa, N. 2005. Generalized Histogram Intersection Kernel for Image Recognition. In ICIP, Genova, Italy.
  • VLFeat - An open and portable library of computer vision Algorithms