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Bayesian texture classification method using a random sampling scheme
We present a texture classification approach that uses a Bayesian inference procedure using local co-occurrence properties over a set of randomly sampled points as evidence. Prior probabilities are modelled using gray level co-occurrence matrices (GLCMs) in a number of distances and orientations. By...
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Main Authors: | , , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | We present a texture classification approach that uses a Bayesian inference procedure using local co-occurrence properties over a set of randomly sampled points as evidence. Prior probabilities are modelled using gray level co-occurrence matrices (GLCMs) in a number of distances and orientations. By using Bayes' rule, we find texture class that maximizes a posteriori probability of the observed gray level intensity pair in a randomly chosen point. Each point casts a vote for the texture that best explains observed co-occurrence properties. A majority voting procedure assigns a winning label for a texture class. Our approach results in a fast classifier because it does not need to compute GLCM for the texture under test. Our method was tested on a subset of textures from Brodatz database and the classifier accuracy was estimated at about 85% even when a small fraction of points in the image under test were used for the classification phase. |
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ISSN: | 1062-922X 2577-1655 |
DOI: | 10.1109/ICSMC.2003.1244188 |