CFP last date
20 May 2024
Reseach Article

Region based Image Similarity using Fuzzy based SIFT Matching

by Shashikanth C C, Parag Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 3
Year of Publication: 2013
Authors: Shashikanth C C, Parag Kulkarni
10.5120/11379-6655

Shashikanth C C, Parag Kulkarni . Region based Image Similarity using Fuzzy based SIFT Matching. International Journal of Computer Applications. 67, 3 ( April 2013), 47-50. DOI=10.5120/11379-6655

@article{ 10.5120/11379-6655,
author = { Shashikanth C C, Parag Kulkarni },
title = { Region based Image Similarity using Fuzzy based SIFT Matching },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 3 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 47-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number3/11379-6655/ },
doi = { 10.5120/11379-6655 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:23:43.618962+05:30
%A Shashikanth C C
%A Parag Kulkarni
%T Region based Image Similarity using Fuzzy based SIFT Matching
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 3
%P 47-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a region based approach using Scale In-variant Feature Transform (SIFT) and fuzzy logic for computing region based image similarity. When SIFT algorithm is used for matching im¬ages, a meaningful or semantic match according to human perception is not obtained. Hence to refine its output, a region based approach has been proposed. When a test image is compared with reference image, SIFT descriptors are computed and the images are segmented into regions and labeled. SIFT similarity measure along with the region information is given as input to fuzzy logic to determine region based similarity mea¬sure. Experiments are done using real world optical images, Caltech image datasets and a few satellite images. The proposed approach is found to be efficient for opti-cal images, Caltech image datasets and have good performance for satellite images.

References
  1. Foschi, P. G. , Kolippakkam, D. , Liu, H. , Mandvikar, A. , 2003, Feature extraction for image mining, Joint Conference on Information Sciences (JCIS 2003)
  2. Addison, J. F. D. , Wermter, S. , Arevian, G. Z. 2003, A comparison of feature extraction and selection techniques, International Conference on Artificial Neural Networks
  3. Roth, P. M. , Winter, M. 2008, Survey of appearance-based methods for object recog¬nition phd thesis, Inst. for Computer Graphics and Vision, Graz University of Technology, Austria
  4. Zitova, B. , Flusser, J. , 2003, Image registration methods: a survey, Image and Vision Computing (2003), 977–1000
  5. Lowe, D. G. 2004, Distinctive image features from scale-invariant keypoints, Proceedings of the International conference of Computer Vision, Corfu 60 (2004) 91–110
  6. Park, D. K. , Jeon, Y. S. , Won, C. S, 2000, Efficient use of local edge histogram descrip¬tor, Proceedings of the 2000 ACM workshops on Multimedia, Los Angeles, California, United States, 51–54
  7. Squire, D. M. , 1998, Learning a similarity-based distance measure for image database organization from human partitionings of an image set, IEEE Workshop on Applications of Computer Vision (WACV98), Princeton, NJ, USA,. 88–93
  8. Caltech, 2012, http://www. vision. caltech. edu/ Image_Datasets/Caltech256/
  9. Wang, 2012, http://wang. ist. psu. edu/docs/related/
  10. NASA, 2012, http://eol. jsc. nasa. gov/
Index Terms

Computer Science
Information Sciences

Keywords

SIFT descriptors Fuzzy logic Region based image similarity