Title :
Fuzzy support vector machines for image classification fusing MPEG-7 visual descriptors
Author :
Spyrou, Evaggelos ; Stamou, Giorgos ; Avrithis, Yannis ; Kollias, Stefanos
Author_Institution :
Image, Video & Multimedia Syst. Lab., Nat. Tech. Univ. of Athens, Zografou, Greece
fDate :
Nov. 30 2005-Dec. 1 2005
Abstract :
This paper proposes a new type of a support vector machine which uses a kernel constituted from fuzzy basis functions. The proposed network combines the characteristics both of a support vector machine and a fuzzy system: high generalization performance, even when the dimension of the input space is very high, structured and numerical representation of knowledge and ability to extract linguistic fuzzy rules, in order to bridge the "semantic gap" between the low-level descriptors and the high-level semantics of an image. The Fuzzy SVM network was evaluated using images from the aceMedia Repository and more specifically in a beach/urban scenes classification problem.
Keywords :
computational linguistics; fuzzy logic; image classification; image coding; support vector machines; MPEG-7 visual descriptors; aceMedia Repository; beach scenes classification problem; fuzzy basis function; fuzzy support vector machine; image classification; linguistic fuzzy rules; numerical representation; urban scenes classification problem;
Conference_Titel :
Integration of Knowledge, Semantics and Digital Media Technology, 2005. EWIMT 2005. The 2nd European Workshop on the (Ref. No. 2005/11099)
Conference_Location :
London
Print_ISBN :
0-86341-595-4