Wavelet Transform for the Analysis of Convolutional Neural Networks in Texture Recognition

Convolutional neural networks have become omnipresent in applications of image recognition during the last years. However, when it comes to texture analysis, classical techniques developed before the popularity of deep learning has demonstrated potential to boost the performance of these networks, especially when they are employed as feature extractor. Given this context, here we propose a novel method to analyze feature maps of a convolutional network by wavelet transform. In the first step, we compute the detail coefficients from the activation response on the penultimate layer. In the second one, a one-dimensional version of local binary patterns are computed over the details to provide a local description of the frequency distribution. The frequency analysis accomplished by wavelets has been reported to be related to the learning process of the network. Wavelet details capture finer features of the image without increasing the number of training epochs, which is not possible, in feature extractor mode. This process also attenuates over-fitting effect at the same time that preserves the computational efficiency of feature extraction. Wavelet details are also directly related to fractal dimension, an important feature of textures and that has also recently been found to be related to generalization capabilities. The proposed methodology was evaluated on the classification of benchmark databases as well as in a real-world problem (identification of plant species), outperforming the accuracy of the original architecture and of several other state-of-the-art approaches.