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dc.contributor.authorSong, Yang
dc.contributor.authorLi, Qing
dc.contributor.authorFeng, Dagan
dc.contributor.authorZou, Ju Jia
dc.contributor.authorCai, Weidong
dc.date.accessioned2019-12-18T00:59:01Z
dc.date.available2019-12-18T00:59:01Z
dc.date.issued2016-11-15
dc.identifier.citationSong, Y., Li, Q., Feng, D. et al. Comp. Visual Media (2016) 2: 367. https://doi.org/10.1007/s41095-016-0060-6en_AU
dc.identifier.issn2096-0662
dc.identifier.urihttps://ses.library.usyd.edu.au/handle/2123/21553
dc.description.abstractTexture provides an important cue for many computer vision applications, and texture image classification has been an active research area over the past years. Recently, deep learning techniques using convolutional neural networks (CNN) have emerged as the state-of-the-art: CNN-based features provide a significant performance improvement over previous handcrafted features. In this study, we demonstrate that we can further improve the discriminative power of CNN-based features and achieve more accurate classification of texture images. In particular, we have designed a discriminative neural network-based feature transformation (NFT) method, with which the CNN-based features are transformed to lower dimensionality descriptors based on an ensemble of neural networks optimized for the classification objective. For evaluation, we used three standard benchmark datasets (KTH-TIPS2, FMD, and DTD) for texture image classification. Our experimental results show enhanced classification performance over the state-of-the-art.en_AU
dc.publisherSpringeren_AU
dc.relationARC DE150101655en_AU
dc.rights© The Author(s) 2016 The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_AU
dc.subjecttexture classification; neural networks; feature learning; feature transformationen_AU
dc.titleTexture image classification with discriminative neural networksen_AU
dc.typeArticleen_AU
dc.identifier.doi10.1007/s41095-016-0060-6
dc.type.pubtypePublisher versionen_AU


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