Fractal pooling: A new strategy for texture recognition using convolutional neural networks

Texture recognition is an important task in computer vision and, as most problems in the area nowadays, has benefited from the use of deep convolutional neural networks. Nevertheless, typical architectures designed for object recognition usually do not perform optimally in such tasks. One of the most important elements of deep architectures in this application concerns the pooling operation. In particular, the well established global average pooling fails to capture more complex, multiscale and non-linear relation among the output activations. In this context, we propose fractal pooling, where the average is replaced by fractal dimension of the feature map. The new module is coupled with a convolutional backbone and a trainable residual block. The method is evaluated on texture classification, both on benchmark databases and on a real-world problem in botany. Our results are competitive with the state-of-the-art in texture classification, outperforming several modern deep learning approaches in terms of classification accuracy with the addition of minimal computational burden. The results suggest the potential of the proposed methodology for texture recognition in general and that using complexity measures is a promising strategy to perform pooling of deep features for this type of image.