Abstract
This paper presents an unsupervised dynamic colour texture segmentation method with unknown and variable number of texture classes. Single regions with dynamic textures can furthermore change their location as well as their shape. Individual dynamic multispectral texture mosaic frames are locally represented by Markovian features derived from four directional multispectral Markovian models recursively evaluated for each pixel site. Estimated frame-based Markovian parametric spaces are segmented using an unsupervised segmenter derived from the Gaussian mixture model data representation which exploits contextual information from previous video frames segmentation history. The segmentation algorithm for every frame starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The presented method is objectively numerically evaluated on the dynamic textural test set from the Prague Segmentation Benchmark.
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Haindl, M., Mikeš, S. (2013). Unsupervised Dynamic Textures Segmentation. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_52
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DOI: https://doi.org/10.1007/978-3-642-40261-6_52
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