Wavelet Convolutional Neural Networks for Texture Classification

TL;DR

This paper introduces wavelet CNNs, a novel architecture that integrates spectral analysis via wavelet transforms into CNNs, significantly improving texture classification accuracy while reducing model complexity.

Contribution

The paper proposes a new wavelet CNN architecture that combines spectral analysis with CNNs, enhancing texture classification performance and efficiency.

Findings

Wavelet CNNs outperform existing models in texture classification accuracy.

Wavelet CNNs have fewer parameters, making them easier to train.

Spectral analysis integration improves texture feature extraction.

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.