15 February 2013 Unsupervised texture segmentation based on latent topic assignment
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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.
© 2013 SPIE and IS&T
Hao Feng, Hao Feng, Zhiguo Jiang, Zhiguo Jiang, Jun Shi, 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 . Submission:


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