A Handcrafted Normalized-Convolution Network for Texture Classification

Vu-Lam Nguyen 1 Ngoc-Son Vu 1 Philippe-Henri Gosselin 2, 1
2 MIDI
ETIS - Equipes Traitement de l'Information et Systèmes
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.
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Communication dans un congrès
Compact and Efficient Feature Representation and Learning in Computer Vision, Oct 2017, Venice, Italy
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Contributeur : Philippe-Henri Gosselin <>
Soumis le : jeudi 11 janvier 2018 - 14:08:08
Dernière modification le : mercredi 11 avril 2018 - 15:10:08

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Vu-Lam Nguyen, Ngoc-Son Vu, Philippe-Henri Gosselin. A Handcrafted Normalized-Convolution Network for Texture Classification. Compact and Efficient Feature Representation and Learning in Computer Vision, Oct 2017, Venice, Italy. 〈hal-01681182〉

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