WaveTexNeT: Ensemble Based Wavelet-Xception Deep Neural Network Architecture for Color Texture Classification
Philomina Simon, Uma Vijayasundaram · Traitement du signal · 2022
Recognizing real visual textures in the nature have been a challenging task since they are complex and stochastic.In spite of several decades of research, classifying the real world color textures are still challenging because of the intricate nature of the textures and the lack of substantial improvement of accuracy in benchmark datasets.Deep Learning techniques have found to be effective in identifying and classifying the texture patterns to a larger extent, but it could not capture spectral information and achieve excellent results for natural images.In this paper, we propose a deep convolutional neural network architecture, WaveTexNeT that combines Wavelet convolutional neural networks (WaveletCNN) and Xception model with luminance information for classifying real-world natural textures.Spectral and spatial features are extracted from WaveletCNN and Xception model.The highlight of the work is the utilization of spectral and spatial information along with luminance for texture classification.A color space image data augmentation technique is proposed that use luminance images from YIQ model for color texture classification.This work also throws light into the significance of luminance information for texture classification.Experimental analysis of the work reports that WaveTexNeT captures better feature representations and outperforms the accuracy obtained using the state-of-the-art methods.WaveTexNeT obtained an accuracy of 90.34% and 95.01% for the describable and material perception texture datasets DTD and FMD respectively.