Unsupervised clustering of texture features using SOM and Fourier transform

Author

Verma, Brijesh ; Muthukkumarasamy, Vallipuram ; He, Changming

Author_Institution

Sch. of Inf. Technol., Griffith Univ., Gold Coast, Qld., Australia

Volume

2

fYear

2003

fDate

20-24 July 2003

Firstpage

1237

Abstract

Texture analysis has a wide range of real-world applications. This paper presents a novel technique for texture feature extraction and compares its performance with a number of other existing techniques using a benchmark image database. The proposed feature extraction technique uses 2D-DFT transform and self-organizing map (SOM). A combination of 2D-DFT and SOM with optimal parameter settings produced very promising results. The results from large sets of experiments and detailed analysis are included in this paper.

Keywords

benchmark testing; discrete Fourier transforms; feature extraction; image texture; pattern clustering; self-organising feature maps; visual databases; 2D discrete Fourier transform; benchmark image database; self-organizing map; texture analysis; texture feature extraction; texture feature unsupervised clustering; Discrete Fourier transforms; Feature extraction; Fourier transforms; Image databases; Image retrieval; Image segmentation; Image texture analysis; Pixel; Spatial databases; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223870

Filename

1223870