Unsupervised texture segmentation based on latent topic assignment

Hao Feng; Zhiguo Jiang; Jun Shi

doi:10.1117/1.JEI.22.1.013026

15 February 2013 Unsupervised texture segmentation based on latent topic assignment

Hao Feng, Zhiguo Jiang, Jun Shi

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 1, 013026 (February 2013). https://doi.org/10.1117/1.JEI.22.1.013026

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.

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Hao Feng, Zhiguo Jiang, and Jun Shi "Unsupervised texture segmentation based on latent topic assignment," Journal of Electronic Imaging 22(1), 013026 (15 February 2013). https://doi.org/10.1117/1.JEI.22.1.013026

Published: 15 February 2013

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