Textures-ALOT Dataset

Dataset details

Last updated: 15 Dec 2022
Meta Album ID MNF.TEX_ALOT
Domain ID MNF
Domain Name Manufacturing
Set Number 2
Dataset ID TEX_ALOT
Dataset Name Textures-ALOT
Short Description Textures dataset from Amsterdam Library of Textures (ALOT)
Long Description Textures ALOT dataset (https://aloi.science.uva.nl/public_alot/) consists of 27 500 images from 250 categories. The images in the dataset are captured in controlled environment by the creators of the dataset. The images have different viewing angle, illumination angle, and illumination color for each material of texture. A preprocessed version of Textures-ALOT is used in the Meta-Album meta-dataset. The images are first cropped into square images and then resized to 128x128 with anti-aliasing filter.
# Classes 250
# Images 25000
Keywords textures, manufacturing
Data Format images
Image size 128x128
License
(original data release)		 Open for research, cite paper to use dataset
License
(Meta-Album data release)		 CC BY-NC 4.0
License URL
(Meta-Album data release)		 https://creativecommons.org/licenses/by-nc/4.0/
Source Amsterdam Library of Textures (ALOT), University of Amsterdam, Netherlands
Source URL https://aloi.science.uva.nl/public_alot/
Original Author Gertjan Burghouts, Jan-Mark Geusebroek
Original contact geusebroek@uva.nl
Meta Album author Ihsan Ullah
Created Date 01 March 2022
Contact Name Ihsan Ullah
Contact Email meta-album@chalearn.org
Contact URL https://meta-album.github.io/

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Dataset Info Datasheet

Download Dataset from OpenML

Dataset Version OpenML ID
Micro 44274 Download
Mini 44304 Download
Extended 44337 Download

Code to download dataset using OpenML API

      # import openml
      import openml
  
      # download dataset with DATASET_ID. DATASET_ID is OpenML ID
      dataset = openml.datasets.get_dataset(DATASET_ID)
  
      # display dataset info
      print(dataset.name)

Sample Images

Cite this dataset 

@article{BURGHOUTS2009306,
    title = {Material-specific adaptation of color invariant features},
    journal = {Pattern Recognition Letters},
    volume = {30},
    number = {3},
    pages = {306-313},
    year = {2009},
    issn = {0167-8655},
    doi = {https://doi.org/10.1016/j.patrec.2008.10.005},
    url = {https://www.sciencedirect.com/science/article/pii/S0167865508003073},
    author = {Gertjan J. Burghouts and Jan-Mark Geusebroek},
    keywords = {Image modeling, Color, Codebook representation, Texture, Textons},
    abstract = {For the modeling of materials, the mapping of image features onto a codebook of feature representatives receives extensive treatment. For reason of their generality and simplicity, filterbank outputs are commonly used as features. The MR8 filterbank of Varma and Zisserman is performing well in a recent evaluation. In this paper, we construct color invariant filter sets from the original MR8 filterbank. We evaluate several color invariant alternatives over more than 250 real-world materials recorded under a variety of imaging conditions including clutter. Our contribution is a material recognition framework that learns automatically for each material specifically the most discriminative filterbank combination and corresponding degree of color invariance. For a large set of materials each with different physical properties, we demonstrate the material-specific filterbank models to be preferred over models with fixed filterbanks.}
}
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Cite Meta-Album

  @inproceedings{meta-album-2022,
    title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},
    author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh},
    booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
    url = {https://meta-album.github.io/},
    year = {2022}
  }
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