A texture extraction technique using 2D-DFT and Hamming distance

Author

Tao, Yu ; Muthukkumarasamy, Vallipuram ; Verma, Brijesh ; Blumenstein, Michael

Author_Institution

Sch. of Inf. Technol., Griffith Univ., Australia

fYear

2003

fDate

27-30 Sept. 2003

Firstpage

120

Lastpage

125

Abstract

Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. This paper presents a novel technique for texture extraction and classification. The proposed feature extraction technique uses 2D-DFT transformation. A combination of this technique and a Hamming Distance based neural network for classification of extracted features is investigated. The experimental results on a benchmark database and detailed analysis are presented.

Keywords

discrete Fourier transforms; feature extraction; graph theory; image classification; neural nets; 2D-DFT transformation; Hamming distance; benchmark database; computer vision; discrete Fourier transform; feature classification; feature extraction; neural network; textural properties; texture analysis; texture classification; texture extraction; Data mining; Feature extraction; Fourier transforms; Hamming distance; Image color analysis; Image databases; Image segmentation; Image texture analysis; Spatial databases; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003. Proceedings. Fifth International Conference on

Print_ISBN

0-7695-1957-1

Type

conf

DOI

10.1109/ICCIMA.2003.1238111

Filename

1238111