A Handcrafted Normalized-Convolution Network for Texture Classification

Vu-Lam Nguyen, Ngoc-Son Vu, Philippe-Henri Gosselin; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1238-1245

Abstract


In this paper, we propose a Handcrafted Normalized-Convolution Network (NmzNet) for efficient texture classification. NmzNet is implemented by a three-layer normalized convolution network, which computes successive normalized convolution with a predefined filter bank (Gabor filter bank) and modulus non-linearities. Coefficients from different layers are aggregated by Fisher Vector aggregation to form the final discriminative features. The results of experimental evaluation on three texture datasets UIUC, KTH-TIPS-2a, and KTH-TIPS-2b indicate that our proposed approach achieves the good classification rate compared with other handcrafted methods. The results additionally indicate that only a marginal difference exists between the best classification rate of recent frontiers CNN and that of the proposed method on the experimented datasets.

Related Material


[pdf]
[bibtex]
@InProceedings{Nguyen_2017_ICCV,
author = {Nguyen, Vu-Lam and Vu, Ngoc-Son and Gosselin, Philippe-Henri},
title = {A Handcrafted Normalized-Convolution Network for Texture Classification},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}