A robust-texture convolutional neural network

(ii) similarly shaped objects with different textures of images are often assigned into different classesand (iii) different shaped objects with similar textures of images are often assigned into the same class. The proposed scheme embeded a texture-embedded supplementary method, composed of texture compensation and supplement, into the CNN architecture. The texture compensation is constructed from texture subbands decomposed by 2D Littlewood-Paley empirical wavelet transform (2D Littlewood-Paley EWT). Then the texture supplement is constructed from texture subbands by using Gabor wavelet to extract multi-scale and multi-orientation texture features. Based on two challenging datasets, the experimental results show that RT-CNN outperforms all test baseline methods: AlexNet, T-CNN, and wavelet-CNN, in terms of recognition accuracy rate. On a typical dataset, the recognition accuracy rate of the proposed method is still better than those of T-CNN and waveletCNN, and is comparable to that of AlexNet