An Efficient Texture Classification Algorithm with Illumination, Rotation and Scale Invariance

The variation of illumination, rotation and scale in textures makes texture classification a challenging problem. Traditional texture classification algorithms have weaknesses in terms of handling illumination, rotation, scale changes, and providing real-time feedback. Therefore, we presented an efficient illumination, rotation and scale invariant texture classification algorithm. First, a scale space was constructed by the original image and its two Gauss filtered images. Second, the completed local binary pattern with dominant direction in neighborhood (DDN-CLBP) algorithm was used to extract the illumination and rotation invariant features in the images with different scales in the scale space. Third, scale invariant features were obtained by taking the maximum value in each pattern across different scales. Finally, the nearest subspace classifier was used to perform classification. The experimental results on five representative texture databases show that the proposed algorithm can handle illumination, rotation and scale variation well without pre-learning, and it is highly efficient.