Wavelet Convolutional Neural Networks for Texture Classification

Abstract

Texture classification is an important and challenging problem in many image processing applications. While convolutional neural networks (CNNs) achieved significant successes for image classification, texture classification remains a difficult problem since textures usually do not contain enough information regarding the shape of object. In image processing, texture classification has been traditionally studied well with spectral analyses which exploit repeated structures in many textures. Since CNNs process images as-is in the spatial domain whereas spectral analyses process images in the frequency domain, these models have different characteristics in terms of performance. We propose a novel CNN architecture, wavelet CNNs, which integrates a spectral analysis into CNNs. Our insight is that the pooling layer and the convolution layer can be viewed as a limited form of a spectral analysis. Based on this insight, we generalize both layers to perform a spectral analysis with wavelet transform. Wavelet CNNs allow us to utilize spectral information which is lost in conventional CNNs but useful in texture classification. The experiments demonstrate that our model achieves better accuracy in texture classification than existing models. We also show that our model has significantly fewer parameters than CNNs, making our model easier to train with less memory.

9 pages, 7 figures, 2 tables

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Striving for Simplicity: The All Convolutional Net
• Return of the Devil in the Details: Delving Deep into Convolutional Nets
• Texture Synthesis Using Convolutional Neural Networks

Cited by in corpus (14)

• Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
• SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification
• Photorealistic Style Transfer via Wavelet Transforms
• Compressing the Input for CNNs with the First-Order Scattering Transform
• Deep Learning in the Wavelet Domain
• MobileStyleGAN: A Lightweight Convolutional Neural Network for High-Fidelity Image Synthesis
• Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?
• SpecNet: Spectral Domain Convolutional Neural Network
• Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications
• Harmonic Networks: Integrating Spectral Information into CNNs
• Wavelet based edge feature enhancement for convolutional neural networks
• Wavelet-Based Dual-Branch Network for Image Demoireing
• Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution
• On-device Filtering of Social Media Images for Efficient Storage