Evaluating color texture descriptors under large variations of controlled lighting conditions
This work addresses the challenge of robust texture recognition for computer vision applications, but it is incremental as it focuses on systematic evaluation rather than introducing new methods.
The paper tackled the problem of recognizing color texture under varying lighting conditions by conducting an extensive comparison of old and new texture features, with and without color normalization, and found that the evaluation was performed on a new database of 68 raw food samples under 46 lighting variations.
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.