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Reseach Article

A New Approach of Feature Extraction using Genetic Algorithm and SIFT

by Kapil Kumar Pachouri, Atul Barve
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 21
Year of Publication: 2015
Authors: Kapil Kumar Pachouri, Atul Barve
10.5120/21853-5176

Kapil Kumar Pachouri, Atul Barve . A New Approach of Feature Extraction using Genetic Algorithm and SIFT. International Journal of Computer Applications. 122, 21 ( July 2015), 42-45. DOI=10.5120/21853-5176

@article{ 10.5120/21853-5176,
author = { Kapil Kumar Pachouri, Atul Barve },
title = { A New Approach of Feature Extraction using Genetic Algorithm and SIFT },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 21 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 42-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number21/21853-5176/ },
doi = { 10.5120/21853-5176 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:11.260032+05:30
%A Kapil Kumar Pachouri
%A Atul Barve
%T A New Approach of Feature Extraction using Genetic Algorithm and SIFT
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 21
%P 42-45
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Feature Extraction is a process of extracting the point from the image such that the image can be compared and recognized easily and quickly. Since various feature extraction techniques are implemented so that it can be used for variety of applications such as face recognition. The existing technique implemented for the extraction of features such as SIFT, SURF are efficient in terms of accuracy of number of features extraction but can't extracted all features from different types of images. Hence an efficient technique is implemented here for the efficient features extracted from images using combinatorial method of optimizing SIFT features extraction technique so that more number of features can be extracted.

References
  1. N. C. Yang, W. H. Chang, C. M. Kuo, et al. , "A fast MPEG-7 dominant colour extraction with new similarity measure for image retrieval", Journal of Visual Comm. and Image Retrieval, vol. 19, (2008), pp. 92-105.
  2. M. M. Islam, D. Zhang and G. Lu. "A geometric method to compute directionality features for texture images", In Proc. ICME, (2008), pp. 1521-1524.
  3. S. Arivazhagan and L. Ganesan, "Texture classification using wavelet transform", Pattern Recognition Letters, vol. 24, (2003), pp. 1513-1521.
  4. H. J. Baeand S. H. Jung, Image Retrieval Using Texture Based on DCT,Proc. Of the international conference on Information and communications security, vol. 2pp. 1065-1068,1997
  5. Erkan Bostanci, NadiaKanwal, Adrian F. Clark, "Spatial Statistics of Image Features for Performance Comparison" IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 23, NO. 1, JANUARY 2014.
  6. Jean-Philippe Vert, Tomoko Matsui, "High-Level Feature Extraction Using Svm With Walk-Based Graph Kernel, 2007.
  7. Minh Hoai Nguyen, Fernando de la Torre, "Optimal feature selection for support vector machines" Pattern Recognition 43, 584–591, 2010.
  8. Ales Prochazka and Jarom?r Kukal, "Wavelet Transform Use for Feature Extraction and EEG Signal Segments Classi?cation.
  9. Suganthy, M. and P. Ramamoorthy, "Principal Component Analysis Based Feature Extraction, Morphological Edge Detection and Localization for Fast Iris Recognition" , Journal of Computer Science 8 (9): 1428-1433, 2012 ISSN 1549-3636.
Index Terms

Computer Science
Information Sciences

Keywords

SIFT Genetic Algorithm SURF HFE SVM.