无监督图像分割的直方图聚类

J. Puzicha, J. Buhmann, Thomas Hofmann
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引用次数: 94

摘要

本文介绍了一种新的分布(直方图)数据概率分组统计混合模型。采用贝叶斯框架,我们提出了退火最大后验估计来计算最优聚类解。为了加快优化过程,开发了一种高效的多尺度配方。我们提出了一个基于Gabor系数局部分布的纹理图像无监督分割的原型应用。基准测试结果表明,与K-means聚类和基于接近度的算法相比,该算法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Histogram clustering for unsupervised image segmentation
This paper introduces a novel statistical mixture model for probabilistic grouping of distributional (histogram) data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an efficient multiscale formulation is developed. We present a prototypical application of this method for the unsupervised segmentation of textured images based on local distributions of Gabor coefficients. Benchmark results indicate superior performance compared to K-means clustering and proximity-based algorithms.
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