CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach 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

by Swathi Rao G, Anuj Sharma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 2
Year of Publication: 2013
Authors: Swathi Rao G, Anuj Sharma
10.5120/12711-9517

Swathi Rao G, Anuj Sharma . 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 ( July 2013), 5-12. DOI=10.5120/12711-9517

@article{ 10.5120/12711-9517,
author = { Swathi Rao G, Anuj Sharma },
title = { 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 },
issue_date = { July 2013 },
volume = { 73 },
number = { 2 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number2/12711-9517/ },
doi = { 10.5120/12711-9517 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:39:29.823259+05:30
%A Swathi Rao G
%A Anuj Sharma
%T Cost Parameter Analysis and Comparison of Linear Kernel and Hellinger Kernel Mapping of SVM on Image Retrieval and Effects of Addition of Positive Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 2
%P 5-12
%D 2013
%I Foundation of Computer Science (FCS), NY, 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
  1. 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,.
  2. Berkeley, U. C. , Alexander C. , and Malik, J. Classification Using Intersection Kernel Support Vector Machines is Efficient.
  3. 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.
  4. Durgesh , k. , and lekha, B. Data Classification Using Support Vector Machine. Journal of Theoretical and Applied Information Technology.
  5. 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.
  6. 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.
  7. Claudio , C. , Ciocca, G. , and Schettini, R. Image annotation using SVM.
  8. Gidudu , A. , Tshilidzi, M. , and Gregg, H. Image Classification Using SVMs One-against-One Vs One-against-All.
  9. 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.
  10. Boughorbel, S. , Tarel, J. P. , and Boujemaa, N. 2005. Generalized Histogram Intersection Kernel for Image Recognition. In ICIP, Genova, Italy.
  11. VLFeat - An open and portable library of computer vision Algorithms
Index Terms

Computer Science
Information Sciences

Keywords

Cost parameter Hellinger Kernel Image Classification Image Retrieval Kernel Functions Linear kernel SIFTS Support Vector Machine

Powered by PhDFocusTM