TY  - CONF
AU  - Muhammad Anwer Rao
AU  - Fahad Shahbaz Khan
AU  - Joost Van de Weijer
AU  - Jorma Laaksonen
A2  - ACM
PY  - 2017//
TI  - Tex-Nets: Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition
BT  - 19th International Conference on Multimodal Interaction
KW  - Convolutional Neural Networks
KW  - Texture Recognition
KW  - Local Binary Paterns
N2  - Recognizing materials and textures in realistic imaging conditions is a challenging computer vision problem. For many years, local features based orderless representations were a dominant approach for texture recognition. Recently deep local features, extracted from the intermediate layers of a Convolutional Neural Network (CNN), are used as filter banks. These dense local descriptors from a deep model, when encoded with Fisher Vectors, have shown to provide excellent results for texture recognition. The CNN models, employed in such approaches, take RGB patches as input and train on a large amount of labeled images. We show that CNN models, which we call TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard deep models trained on RGB patches. We further investigate two deep architectures, namely early and late fusion, to combine the texture and color information. Experiments on benchmark texture datasets clearly demonstrate that TEX-Nets provide complementary information to standard RGB deep network. Our approach provides a large gain of 4.8%, 3.5%, 2.6% and 4.1% respectively in accuracy on the DTD, KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets, compared to the standard RGB network of the same architecture. Further, our final combination leads to consistent improvements over the state-of-the-art on all four datasets.
L1  - http://refbase.cvc.uab.es/files/RKW2017.pdf
UR  - http://dx.doi.org/10.1145/3078971.3079001
N1  - LAMP; 600.109; 600.068; 600.120
ID  - Muhammad Anwer Rao2017
ER  -