Outex - new framework for empirical evaluation of texture analysis algorithms

By Timo Ojala, Topi Mäenpää, Matti Pietikäinen, Jaakko Viertola, Juha Kyllönen, Sami Huovinen. Object recognition supported by user interaction for service robots, 2002

766 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9f539faad1635493f46ab6638eaca6ebc71f3ff5

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In Computer Vision and Pattern Recognition

Citations in the corpus

A comparative study on multivariate mathematical morphology (2007, methodology): “As far as texture classification is concerned, here we employ the colour textures of Outex13 (figure 6) [59].”
Radon transform orientation estimation for rotation invariant texture analysis (2005, methodology): “Then, we rotate the image by to adjust the orientation of the texture.”
Enriching Texture Analysis with Semantic Data (2013, methodology): “The Outex dataset [18] was adopted for our analysis.”
Blur Insensitive Texture Classification Using Local Phase Quantization (2008, methodology): “For some reason the result of the Gabor method for the same test suite was two percentages better in the experiments in [11], where the Gabor method gave the best result.”
Combined scattering for rotation invariant texture analysis (2012, methodology): “Texture classification experiments are performed on the OUTEX10 database [10] (rot experiment).”
Local binary features for texture classification: Taxonomy and experimental study (2017, unknown): “The six classes of our taxonomy”
A stable multi-scale kernel for topological machine learning (2014, methodology): “For texture recognition, we use the OuteLTC19(\H91919 benchmark [21], downsampled to 32x32 pixel images.”
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features (2009, methodology): “In the ﬁrst experiment, we used the Outex TC 0012 [7] 1.”
A Completed Modeling of Local Binary Pattern Operator for Texture Classification (2010, unknown): “* Corresponding author, phone: 852-27667271, fax: 852-27740842 representative texture databases [14].”
Median Robust Extended Local Binary Pattern for Texture Classification (2016, methodology): “Outex [48] contains a large collection of surface textures captured under different conditions, which facilitates construction of a wide range of texture analysis problems.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “Then, with the eleven texture attributes, they released a publicly available human-provided labeling of over 300 classes of texture from the Outex database [162].”
An appendix to "Texture databases - A comprehensive survey" (2014, methodology): “Recent references where the datasets described in this section have been used to test texture descriptors are the works of Huang et al. (2013); Hervé et al. (2011); Orlov et al. (2010).”
Classification with color and texture: jointly or separately? (2004, methodology): “The sets included 54 color textures from the Vision Texture database [20], and 68 color textures from the Outex texture database [21].”
Which is the best way to organize/classify images by content? (2007, methodology): “Images are obtained from the Outex dataset [ 19 ].”