Yet Another Computer Vision Index To Datasets (YACVID) - Details

Stand: 2017-11-21 000000m 15:04:37 - Overview

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Name (Institute + Shorttitle)Outex texture bench 
Description (include details on usage, files and paper references)The Outex dataset is part of a framework for empirical evaluation of texture classification and segmentation algorithms.
The framework is being constructed according to the following design principles:

Large versatile image database. The image database contains a large collection of textures, both in form of surface textures and natural scenes. The collection of surface textures exhibits well defined variations to a given reference in terms of illumination, rotation and spatial resolution. Reliable manual segmentation of natural scenes is included in the ground truth data.

A wide range of texture classification and segmentation problems. A large collection of texture classification and segmentation problems, both supervised and unsupervised, is constructed using the image database. The diversity of the surface textures provides a rich foundation for texture classification problems: in addition to īstandardī texture classification, problems of illumination/rotation/resolution invariant texture classification, or their combinations, are also available. Different misclassification cost functions and a prior probabilities of the classes are also incorporated.

Precise problem definition with test suites. Problems are encapsulated into welldefined test suites having precise specifications of input and output data. Specifications are provided in form of general purpose text and image files, hence the user of the framework is not constrained to any given programming environment. Test suites are delivered as individual zip files, which expand to a īstandardizedī directory and file structure.

Trust. We trust other research groups in that they will upload to the site unbiased honest results, which are obtained in accordance with the given test suite specifications.

Collaborative development. We are inviting other research groups to join the effort, for the purpose of a more thorough and efficient long term development of the framework, and to aid in gaining acceptance of the framework in the research community.

Continuing maintenance and refinement. The host research group has a history of about two decades of texture analysis research and current plan is to stay in the business for at least another decade, hence the commitment to maintain and refine the framework is strong.

For more information see: Outex - New framework for empirical evaluation of texture analysis algorithms
http://www.ee.oulu.fi/research/mvmp/mvg/files/pdf/pdf_314.pdf


http://users.ecs.soton.ac.uk/tm1e10/texture.html
http://users.ecs.soton.ac.uk/tm1e10/doc/texture_semantics.zip
http://users.ecs.soton.ac.uk/tm1e10/doc/texture_outex_images.zip

The dataset of pairwise semantic texture comparisons described in the CVPR paper (see above) is available here.


The labelling was performed on 319 texture classes from the Outex dataset, using eleven attributes spaced evenly within the semantic space of texture identified by Bhushan et al. The Texture Lexicon (1997).

The 3,828 texture images (12 samples from each of 319 classes) evaluated are available here. They have been converted to grayscale and resized as in the paper.
 
URL Linkhttp://www.outex.oulu.fi/ 
Files (#)3828 
References (SKIPPED)
Category (SKIPPED) 
Tags (single words, spaced)texture, segmentation, classification, benchmark, synthetic 
Last Changed2017-11-21 
Turing (2.12+3.25=?) :-)