home  |   research   |  members   |  projects  |  publications   |  conferences of interest   |  downloads   |  contact  |  intern
 
 

Salzburg Texture Image Database (STex)

This is "The Multimedia Signal Processing and Security Lab", short WaveLab, website. We are a research group at the Artificial Intelligence and Human Interfaces (AIHI) Department of the University of Salzburg led by Andreas Uhl. Our research is focused on Visual Data Processing and associated security questions. Most of our work is currently concentrated on Biometrics, Media Forensics and Media Security, Medical Image and Video Analysis, and application oriented fundamental research in digital humanities, individualised aquaculture and sustainable wood industry.
Contact:

Description:

The Salzburg Texture Image Database (STex) is a large collection of 476 color texture image that have been captured around Salzburg, Austria. The images have been selected to be used in texture retrieval experiments and are fairly homogeneous. STex is significantly larger than the VisTex texture image database and more homogeneous than databases proposed for texture classification research (Outex, ALOT).

Some example texture images:

STex is available in three different packages:

  1. stex-1024.zip containing 476 1024x1024 color images cropped from the captured images.
  2. stex-512.zip containing 476 512x512 color images downsampled from the 1024x1024 images.
  3. stex-512-splitted.zip containing 7616 128x128 color images obtained by splitting the 512x512 images into 16 non-overlapping tiles.

Each image is named category.n.pnm where category provides a rough categorization of the image (e.g. Wood, Rubber, Food, Misc, etc.) and n (n >= 0) is a running number within the category. Splitted images are named category.n.m.pnm where m (1 <= m <= 16) indicated the m-th tile of the image. Example:
Bark.0012.pnm is the 12th bark image
Bark.0012.09.pnm is the 9th tile of the 12th bark image

The images themselves are stored in uncompressed, raw Netpbm format (.pnm).

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

We like to thank the following contributors: Heinz Hofbauer, Stefan Huber.


Papers:
  • N/A

Software:

Current downloads: