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: Ayala-Ramirez, V., Obara-Kepowicz, M., Sanchez-Yanez, R.E., Jaime-Rivas, R.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Electronic mail
Image databases
Image sampling
Phase estimation
Pixel
Robots
Sampling methods
Testing
Voting
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creator Ayala-Ramirez, V.
Obara-Kepowicz, M.
Sanchez-Yanez, R.E.
Jaime-Rivas, R.
description 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.
doi_str_mv 10.1109/ICSMC.2003.1244188
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subjects Bayesian methods
Electronic mail
Image databases
Image sampling
Phase estimation
Pixel
Robots
Sampling methods
Testing
Voting
title Bayesian texture classification method using a random sampling scheme
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