DocumentCode :
598990
Title :
Local self-Similarity based texture classification
Author :
Hongbo Yang ; Xia Hou
Author_Institution :
Autom. Sch., Beijing Inf. Sci. & Technol. Univ., Beijing, China
fYear :
2012
fDate :
16-18 Oct. 2012
Firstpage :
795
Lastpage :
799
Abstract :
Aim of this paper is to develop a texture classification system for browsing and retrieval of image data. In this paper a novel local self-similarity texture descriptor is presented to describe the local texture pattern. And then, the classifier can be obtained by training local self-similarity texture descriptors captured from different textures. In this paper, the experiments are performed on the Brodatz texture database. And the results demonstrate that the proposed method is very efficient and can achieve high correct classification rate.
Keywords :
fractals; image classification; image retrieval; image texture; visual databases; Brodatz texture database; image data browsing; image data retrieval; local self-similarity texture descriptors training; local self-similarity-based texture classification; local texture pattern; Algorithm design and analysis; Classification algorithms; Databases; Feature extraction; Gabor filters; Image segmentation; Training; AdaBoost; local self-similarity; texture classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2012 5th International Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-0965-3
Type :
conf
DOI :
10.1109/CISP.2012.6469914
Filename :
6469914
Link To Document :
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