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The Raw Food Texture database (RawFooT) has been specially designed to investigate the robustness of descriptors and classification methods with respect to variations in the lighting conditions, with a particular focus on variations in the color of the illuminant. The database includes images of samples of textures, acquired under 46 lighting conditions which may differ in the light direction, in the illuminant color, in its intensity, or in a combination of these factors. Our classes correspond to 68 samples of raw food, including various kind of meat, fish, cereals, fruit etc: the whole database includes 68 x 46 = 3128 images.

Overview of the 68 classes included in the database. For each class it is shown the image taken under D65 at direction Θ=24°.
Overview of the 46 lighting conditions in the database: the top rows represent the flour class while bottom rows represent the currant class.



  1. Claudio Cusano, Paolo Napoletano, and Raimondo Schettini. Evaluating color texture descriptors under large variations of controlled lighting conditions. JOSA A 33.1 (2016): 17-30. (pdf)
  2. Claudio Cusano, Paolo Napoletano, and Raimondo Schettini. Combining multiple features for color texture classification. Journal of Electronic Imaging 25.6 (2016): 061410-061410. (pdf)
  3. Claudio Cusano, Paolo Napoletano, and Raimondo Schettini. Local Angular Patterns for Color Texture Classification. New Trends in Image Analysis and Processing--ICIAP 2015 Workshops. Springer International Publishing, 2015. (pdf)