Texture image classification with discriminative neural networks

Springer Science and Business Media LLC - 2016
Yang Song1, Qing Li1, Dagan Feng1, Ju Jia Zou2, Weidong Cai1
1School of Information Technologies, The University of Sydney, Sydney, Australia
2School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, Australia

Tóm tắt

Texture 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.

Từ khóa


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