DocumentCode :
419605
Title :
Statistical landscape features for texture classification
Author :
Xu, Cun Lu ; Chen, Yan Qiu
Author_Institution :
Dept. of Comput. Sci. & Eng., Fudan Univ., Shanghai, China
Volume :
1
fYear :
2004
fDate :
23-26 Aug. 2004
Firstpage :
676
Abstract :
This paper proposes the use of information derived from the graph of a texture image function for texture description. The graph of an image function is a rumpled surface in the three-dimensional space that appears like a landscape. Four novel texture feature curves are used to characterize the texture. This method is named as statistical landscape features (SLF). SLF achieves a very high correct classification rate of 94.53% on the entire Brodatz set. Besides the very good performance, another remarkable advantage of the proposed method is that it has no parameter to tune.
Keywords :
feature extraction; graph theory; image classification; image texture; statistical analysis; visual databases; Brodatz set; image classification rate; image database; statistical landscape features; texture classification; texture feature curves; texture image function; three dimensional space; Application software; Discrete wavelet transforms; Feature extraction; Filter bank; Gabor filters; Image texture; Image texture analysis; Remote sensing; Signal processing; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-2128-2
Type :
conf
DOI :
10.1109/ICPR.2004.1334262
Filename :
1334262
Link To Document :
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