Texture classification using spectral histograms
- 9 July 2003
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 12 (6) , 661-670
- https://doi.org/10.1109/tip.2003.812327
Abstract
Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage. The distance between two spectral histograms is measured using /spl chi//sup 2/-statistic. The spectral histogram with the associated distance measure exhibits several properties that are necessary for texture classification. A filter selection algorithm is proposed to maximize classification performance of a given dataset. Our classification experiments using natural texture images reveal that the spectral histogram representation provides a robust feature statistic for textures and generalizes well. Comparisons show that our method produces a marked improvement in classification performance. Finally we point out the relationships between existing texture features and the spectral histogram, suggesting that the latter may provide a unified texture feature.Keywords
This publication has 39 references indexed in Scilit:
- Probability models for clutter in natural imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2001
- Texture segmentation using Gaussian-Markov random fields and neural oscillator networksIEEE Transactions on Neural Networks, 2001
- Texture synthesis and pattern recognition for partially ordered Markov modelsPattern Recognition, 1999
- Modeling and classifying color textures using random fields in a random environmentPattern Recognition, 1999
- Minimax Entropy Principle and Its Application to Texture ModelingNeural Computation, 1997
- Learning texture discrimination masksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Gibbs random fields, cooccurrences, and texture modelingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1994
- Performance evaluation for four classes of textural featuresPattern Recognition, 1992
- Texture segmentation using Voronoi polygonsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- Visual Pattern DiscriminationIEEE Transactions on Information Theory, 1962