DocumentCode
2260034
Title
Image Retrieval Based on Fuzzy Kernel Clustering and Invariant Moments
Author
Hao, Pengyi ; Ding, Youdong ; Fang, Yuchun
Author_Institution
Sch. of Comput. Eng. & Sci., ShangHai Univ., Shanghai, China
Volume
1
fYear
2008
fDate
20-22 Dec. 2008
Firstpage
447
Lastpage
452
Abstract
KFCM is a new clustering method, which is applied in pattern classification in some way. However, it has not been applied in the field of content-based image retrieval (CBIR). Considering the predefinition of clustering number and membership matrix is still a hot potato, a novel predefinition approach is proposed in this paper, what´s more, a new CBIR method is suggested and validated. First, values of images are extracted, then they are clustered by fuzzy kernel clustering, and edges are detected by Canny operator, finally, edge invariant moments are calculated and formalized. After that, the Euclidean distance between two images´ formalized moment vectors gives a measure of similarity. Experimental results show that the proposed algorithm having better ability in the anti-noise, and the precision and recall are remarkably improved as well.
Keywords
content-based retrieval; fuzzy set theory; image classification; image retrieval; CBIR method; Canny operator; Euclidean distance; KFCM; clustering number; content-based image retrieval; edge invariant moments; fuzzy kernel clustering; membership matrix; pattern classification; predefinition approach; Application software; Clustering algorithms; Content based retrieval; Image edge detection; Image retrieval; Image segmentation; Information retrieval; Information technology; Kernel; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location
Shanghai
Print_ISBN
978-0-7695-3497-8
Type
conf
DOI
10.1109/IITA.2008.189
Filename
4739613
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