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Texture Analysis Using Multidimensional Histogram

Author(s): Payel Saha | Sudhir Sawarkar

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: icwet;
Issue: 8;
Date: 2012;
Original page

Keywords: Texture classification | multidimensional histograms | vector quantization | self-organizing map | feature selection

Texture features have long been used in remote sensing applications for representing and retrieving regions similar to a query region. Various representations of texture have been proposed based on the power spectrum, grey-level co-occurrence matrices, wavelet features, Gabor features, etc. Analysis of several co-occurring pixel values may benefit texture description but is impeded by the exponential growth of histogram size. Multidimensional histograms can be reduced by using methods like linear compression, dimension optimization and vector quantization. Experiments with natural textures showed that multidimensional histograms provided higher classification accuracies than the channel histograms and the wavelet packet signatures
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