Deep Filter Banks for Texture Recognition and Segmentation

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture attributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, \dcnn, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. \dcnn substantially improves the state-of-the-art in texture, material and scene recognition. Our approach achieves 79.8\% accuracy on Flickr material dataset and 81\% accuracy on MIT indoor scenes, providing absolute gains of more than 10\% over existing approaches. \dcnn easily transfers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, \dcnn can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at localizing ``stuff'' categories and obtains state-of-the-art results on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.