Using Filter Banks in Convolutional Neural Networks for Texture Classification

TL;DR

This paper introduces Texture CNN (T-CNN), a simplified deep learning architecture that leverages filter bank concepts for improved texture classification, reducing computational costs while maintaining high accuracy.

Contribution

The paper proposes a novel T-CNN architecture that emphasizes filter bank features for texture analysis, simplifying the network and enhancing efficiency.

Findings

T-CNN improves texture classification accuracy.

Reduces memory and computational requirements.

Effective alternative to traditional CNNs for texture tasks.

Abstract

Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.