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Journal of Electronic Imaging

Unsupervised texture segmentation based on latent topic assignment
Author(s): Hao Feng; Zhiguo Jiang; Jun Shi
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Paper Abstract

We present an effective solution for unsupervised texture segmentation by taking advantage of the latent Dirichlet allocation (LDA) model. LDA is a generative topic model that is capable of hierarchically organizing discrete data including texts and images. We propose a new texture model by connecting texture primitives to the topic of LDA. The model is able to extract the characteristic features of a texture primitive and group them into a topic based on their frequencies of co-occurrence. Here, the feature descriptor is the connection of Haar-like features of multiple sizes. The segments of an image are finally obtained by identifying the homogeneous regions in the corresponding topic assignment map. The evaluation results for synthetic texture mosaics, remote sensing images, and natural scene images are illustrated.

Paper Details

Date Published: 15 February 2013
PDF: 13 pages
J. Electron. Imag. 22(1) 013026 doi: 10.1117/1.JEI.22.1.013026
Published in: Journal of Electronic Imaging Volume 22, Issue 1
Show Author Affiliations
Hao Feng, BeiHang Univ. (China)
Zhiguo Jiang, BeiHang Univ. (China)
Jun Shi, BeiHang Univ. (China)

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