Texture segmentation using the mixtures of principal component analyzers

Date

2003-01-01

Author

Musa, MEM
Duin, RPW
de Ridder, D
Atalay, Mehmet Volkan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The problem of segmenting an image into several modalities representing different textures can be modelled using Gaussian mixtures. Moreover, texture image patches when translated, rotated or scaled lie in low dimensional subspaces of the high-dimensional space spanned by the grey values. These two aspects make the mixture of local subspace models worth consideration for segmenting this type of images. In recent years a number of mixtures of local PCA models have been proposed. Most of these models require the user to set the number of subspaces and subspace dimensionalities. To make the model autonomous, we propose a greedy EM algorithm to find a suboptimal number of subspaces, besides using a global retained variance ratio to estimate for each subspace the dimensionality that retains the given variability ratio. We provide experimental results for testing the proposed method on texture segmentation.

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URI

https://hdl.handle.net/11511/55762

Journal

COMPUTER AND INFORMATION SCIENCES - ISCIS 2003

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Department of Computer Engineering, Article

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Citation Formats

M. Musa, R. Duin, D. de Ridder, and M. V. Atalay, “Texture segmentation using the mixtures of principal component analyzers,” COMPUTER AND INFORMATION SCIENCES - ISCIS 2003, pp. 505–512, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55762.