SFTA Texture Extractor

Implementation of the SFTA algorithm for texture feature extraction.

Description

Extract texture features from an image using the SFTA (Segmentation-based Fractal Texture Analysis) algorithm. To extract features, use the sfta(I, nt) function, where I corresponds to the input grayscale image and nt is a parameter that defines the size of the feature vector.

The features are returned as a 1 by 6*nt vector.

Example:

I = imread('coins.png');
D = sfta(I, 4)

Brief description of the SFTA algorithm:

The extraction algorithm consists in decomposing the input image into a set of binary images from which the fractal dimensions of the resulting regions are computed in order to describe segmented texture patterns.

Publication where the SFTA algorithm is described:

Costa, A. F., G. E. Humpire-Mamani, A. J. M. Traina. 2012. "An Efficient Algorithm for Fractal Analysis of Textures." In SIBGRAPI 2012 (XXV Conference on Graphics, Patterns and Images), 39-46, Ouro Preto, Brazil.

Acknowledgements

Hausdorff (Box Counting) Fractal Dimension inspired this file.

• Required Products: MATLAB
• MATLAB release: MATLAB 7.14 (R2012a)

[Alceu Costa]

Hello Kaveh,

Thanks for the feedback. The extraction time of 24s that you are getting is considerably higher than the extraction times that I got in my experiments.

Just for comparison, I have just executed with a 720x576 grayscale image, setting the nt parameter to 8 and the extraction time was 6s:

>> tic; sfta(I, 8); toc;
Elapsed time is 6.039394 seconds.

Which value for the nt parameter that you are using? For the datasets that I have employed to test SFTA, I found that a value of nt higher than 8 did not increase classification accuracy.

Here’s a link to the article that I hope can help you:

http://www2.icmc.usp.br/~alceufc/documents/sibgrapi_afc_2012.pdf

[Kaveh Mollazade]

Dear Alceu,

Thank you. This is a nice method to compute the hausdorff's fractal dimension from the border of an object.
I have implemented your method on a data set.
It seems your method is computationally time consuming since for a 720*576 gray scale image, 24.453 second is required for analysis.
Have you any comments about this?

Regards,

Kaveh