Feeding Convolutional Neural Network by hand-crafted features based on Enhanced Neighbor-Center Different Image for color texture classification

Texture analysis has many important applications, including material recognition, face recognition, object detection, image segmentation. Local feature descriptors were the principle approach for texture analysis in the past. Recently, Convolutional Neural Network (CNN) has provided more promising results for texture recognition and other related computer vision tasks. Standard CNN takes labeled RGB images as input. However, other encoded images were used as extra input to CNN, which have been shown to improve the performance. We propose to feed CNN with the new encoded image. The experimental results on four benchmark color texture database show the efficiency of our proposed approach.