Texture image classification with discriminative neural networks

• Published in: Computational visual media (Beijing) Vol. 2; no. 4; pp. 367 - 377
• Main Authors: Song, Yang, Li, Qing, Feng, Dagan, Zou, Ju Jia, Cai, Weidong
• Format: Journal Article
• Language: English
• Published: Beijing Tsinghua University Press 01.12.2016; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Classification; Computer Graphics; Computer Science; Computer vision; Image classification; Image enhancement; Image Processing and Computer Vision; Machine learning; Neural networks; Research Article; Texture; User Interfaces and Human Computer Interaction; feature learning; feature transformation; texture classification; neural networks
• DOI: 10.1007/s41095-016-0060-6
• ISSN: 2096-0433

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.