Abstract
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.
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Yang Song is currently an ARC Discovery Early Career Researcher Award (DECRA) Fellow at the School of Information Technologies, the University of Sydney, Australia. She received her Ph.D. degree in computer science from the University of Sydney in 2013. Her research interests include biomedical imaging informatics, computer vision, and machine learning.
Qing Li is currently an M.Phil. research student at the School of Information Technologies, the University of Sydney, Australia. His research area is deep learning in computer vision and biomedical imaging.
Dagan Feng received his M.E. degree in electrical engineering & computer science (EECS) from Shanghai Jiao Tong University in 1982, M.S. degree in biocybernetics and Ph.D. degree in computer science from the University of California, Los Angeles (UCLA) in 1985 and 1988 respectively, where he received the Crump Prize for excellence in medical engineering. Prof. Feng is currently the head of the School of Information Technologies and the director of the Institute of Biomedical Engineering and Technology, the University of Sydney, Australia. He has published over 700 scholarly research papers, pioneered several new research directions, and made a number of landmark contributions in his field. Prof. Feng’s research in the areas of biomedical and multimedia information technology seeks to address the major challenges in big data science and provide innovative solutions for stochastic data acquisition, compression, storage, management, modeling, fusion, visualization, and communication. Prof. Feng is a Fellow of the ACS, HKIE, IET, IEEE, and Australian Academy of Technological Sciences and Engineering.
Ju Jia Zou received his B.S. and M.S. degrees in radio-electronics from Zhongshan University (also known as Sun Yat-sen University) in Guangzhou, China, in 1985 and 1988, respectively, and Ph.D. degree in electrical engineering from the University of Sydney, Australia, in 2001. Currently, he is a senior lecturer at the School of Computing, Engineering and Mathematics, Western Sydney University, Australia. He was a research associate and then an Australian postdoctoral fellow at the University of Sydney from 2000 to 2003. His research interests include image processing, pattern recognition, computer vision, and their applications. He has been a chief investigator for a number of projects funded by the Australian Research Council. He is a member of the IEEE.
Weidong Cai received his Ph.D. degree in computer science from the Basser Department of Computer Science, the University of Sydney, in 2001. He is currently an associate professor at the School of Information Technologies, and the director of the Multimedia Laboratory, the University of Sydney. He was a lead investigator/visiting professor on medical image analysis and medical computer vision in the Surgical Planning Laboratory (SPL), Harvard Medical School, during his 2014 SSP. His research interests include medical image analysis, image/video processing and retrieval, bioimaging informatics, computational neuroscience, computer vision & pattern recognition, and multimedia computing.
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Song, Y., Li, Q., Feng, D. et al. Texture image classification with discriminative neural networks. Comp. Visual Media 2, 367–377 (2016). https://doi.org/10.1007/s41095-016-0060-6
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DOI: https://doi.org/10.1007/s41095-016-0060-6