# Evaluating color texture descriptors under large variations of controlled lighting conditions

arXiv: https://arxiv.org/abs/1508.01108 · HTML: https://mlsanity.com/paper/1508.01108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2015.

## Built on by (most cited first, 1 of 1)

- [Simulating Personal Food Consumption Patterns using a Modified Markov Chain](https://mlsanity.com/paper/2208.06709/) (2022)

## Abstract (arXiv)

The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed on a new texture database including 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction and intensity. The database allows to systematically investigate the robustness of texture descriptors across a large range of variations of imaging conditions.