Hand-crafted vs learned descriptors for color texture classification

The paper presents a comparison between hand-crafted and learned descriptors for color texture classification. The comparison is performed on five color texture databases that include images under varying imaging conditions: scales, camera orientations, light orientations, light color temperatures, etc. Results demonstrate that learned descriptors, on average, significantly outperform hand-crafted descriptors. However, results obtained on the individual databases show that in the case of Outex 14, that includes training and test images taken under varying illuminant conditions, hand-crafted descriptors perform better than learned descriptors.