Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
3D Texture Recognition Using Bidirectional Feature Histograms
International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
An introduction to nonlinear dimensionality reduction by maximum variance unfolding
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Measuring statistical dependence with hilbert-schmidt norms
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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This work investigates the problem of texture recognition under varying lighting and viewing conditions. One of the most successful approaches for handling this problem is to focus on textons, describing local properties of textures. Leung and Malik [1] introduced the framework of this approach which was followed by other researchers who tried to address its limitations such as high dimensionality of textons and feature histograms as well as poor classification of a single image under known conditions. In this paper, we overcome the above-mentioned drawbacks by use of recently introduced supervised nonlinear dimensionality reduction methods. These methods provide us with an embedding which describes data instances from the same classes more closely to each other while separating data from different classes as much as possible. Here, we take advantage of the superiority of modified methods such as "Colored Maximum Variance Unfolding" as one of the most efficient heuristics for supervised dimensionality reduction. The CUReT (Columbia-Utrecht Reflectance and Texture) database is used for evaluation of the proposed method. Experimental results indicate that the algorithm we have put forward intelligibly outperforms the existing methods. In addition, we show that intrinsic dimensionality of data is much less than the number of measurements available for each item. In this manner, we can practically analyze high dimensional data and get the benefits of data visualization.