This paper presents a comparison of different
Feature Extraction Techniques employed in Content Based
Image Retrieval (CBIR), with its application in Cotton leaf
Disease identification. Firstly, preliminary information about
CBIR architecture is given. The accuracy of key points
detection plays a vital role in the overall results in CBIR
system. So, a comparative analysis of some well-known key
point detectors are presented in this paper. After comparing
them, it is finally concluded that SIFT is the overall best
technique for key points detection in this particular CBIR
system.
Published In : IJCAT Journal Volume 2, Issue 7
Date of Publication : July 2015
Pages : 259 - 268
Figures :09
Tables : --
Publication Link :Comparison of Feature Extraction Techniques in
Cotton Leaf Disease Classification using CBIR
Sushila Palwe : Professor, Computer Department MAEER’s
Maharashtra College of Engineering (MITCOE), Pune affiliated
to Savitribai Phule Pune University.
Chaitanya Budkule : currently pursuing B.E
Computer Engineering from MAEER’s Maharashtra College of
Engineering (MITCOE), Pune affiliated to Savitribai Phule Pune
University. (2014-15 Batch).
Utkarsha Sonawane : currently pursuing B.E Computer
Engineering from MAEER’s Maharashtra College of
Engineering (MITCOE), Pune affiliated to Savitribai Phule Pune
University. (2014-15 Batch).
Vilas Giri : currently pursuing B.E Computer Engineering from
MAEER’s Maharashtra College of Engineunering (MITCOE), P
affiliated to Savitribai Phule Pune University. (2014-15 Batch).
CBIR
Feature Extraction
MSER
SURF
SIFT
ORB
For high Precision and Accuracy, which means lesser false
positives in the output images, MSER is the best algorithm
although its execution time becomes a bottleneck.
SURF is also twice as faster as MSER and SIFT and thus
imposes quite lesser execution overhead but with an
ordinary Precision and Recall. So, if the goal is just high
Accuracy and less execution time, SURF can be used as
Feature Detection and Extraction technique.
[1] J. Matas, O. Chum, M. Urban, and T. Pajdla. "Robust
wide baseline stereo from maximally stable extremal
regions." Proc. of British Machine Vision Conference,
pages 384-396, 2002.
[2] Ryuji Funayama, Hiromichi Yanagihara, Luc Van
Gool, Tinne Tuytelaars, Herbert Bay, "ROBUST
INTEREST POINT DETECTOR AND
DESCRIPTOR", published 2009-09-24
[3] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van
Gool "SURF: Speeded Up Robust Features", Computer
Vision and Image Understanding (CVIU), Vol. 110,
No. 3, pp. 346–359, 2008
[4] Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary
Bradski "ORB: an efficient alternative to SIFT or
SURF", Computer Vision (ICCV), 2011 IEEE
International Conference on. IEEE, 2011
[5] http://en.wikipedia.org/wiki/
[6] www.fp.ucalgary.ca/mhallbey/tutorial.htm