DocumentCode
657999
Title
Hybrid classifier using SIFT descriptor
Author
Kabbai, Leila ; Abdellaoui, Mahmoud ; Douik, Ahmed
Author_Institution
Nat. Eng. Sch. of Monastir-ENIM, Univ. of Monastir, Monastir, Tunisia
fYear
2013
fDate
6-8 May 2013
Firstpage
388
Lastpage
392
Abstract
In this paper, three image classification methods based on Interest Points (IP) will be presented to identify different types of noise attack (Affine, Zoom, Blur and Contrast) between two images using SIFT descriptor. The first one is an affine classifier deduced from several test on a standard images database. The second is a fuzzy classifier based on rules deduced from evolution curves of membership functions. Using the results obtained from the two classifiers, we conceive a third one based on their combination and denoted as “hybrid classifier”. By analyzing the results of the three classifiers, we deduced that the last one leads to best results as we obtained a recognition rate exceeding 95%. This classifier will be considered as good solution to predict the noise attack type.
Keywords
affine transforms; fuzzy set theory; image classification; Affine noise attack; Blur noise attack; Contrast noise attack; SIFT descriptor; Zoom noise attack; affine classifier; fuzzy classifier; hybrid classifier; image classification method; image database; interest points; membership function evolution curve; noise attack type; Databases; Educational institutions; Histograms; IP networks; Noise; Object recognition; Standards; Classifier; SIFT; interest points; matching; noise attack;
fLanguage
English
Publisher
ieee
Conference_Titel
Control, Decision and Information Technologies (CoDIT), 2013 International Conference on
Conference_Location
Hammamet
Print_ISBN
978-1-4673-5547-6
Type
conf
DOI
10.1109/CoDIT.2013.6689576
Filename
6689576
Link To Document