Texture Classification Using 2D LSTM Networks

Abstract

In this paper, we investigate the ability of the Long short term memory (LSTM) recurrent neural network architecture to perform texture classification on images. Existing approaches to texture classification rely on manually designed preprocessing steps or selected feature extractors. Since LSTM networks are able to bridge over long time lags, we propose applying them directly on the image, circumventing any handcrafted pre-processing. We investigate different approaches with several input and output representations. In our experiments on a number of widely used texture benchmarking tasks (KTH-TIPS, OuTex, VisTexL, VisTexP, and Newmarket), we show that the performance is comparable to, or better than, existing state-of-the-art methods for texture classification.