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- W1608175048 abstract "Mixtures of Gaussians are a crucial statistical modeling tool at the heart of many challenging applications in computer vision and machine learning. In this paper, we first describe a novel and efficient algorithm for simplifying Gaussian mixture models using a generalization of the celebrated k-means quantization algorithm tailored to relative entropy. Our method is shown to compare experimentally favourably well with the state-of-the-art both in terms of time and quality performances. Second, we propose a practical enhanced approach providing a hierarchical representation of the simplified GMM while automatically computing the optimal number of Gaussians in the simplified mixture. Application to clustering-based image segmentation is reported." @default.
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- W1608175048 date "2010-01-01" @default.
- W1608175048 modified "2023-09-26" @default.
- W1608175048 title "Levels of Details for Gaussian Mixture Models" @default.
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- W1608175048 doi "https://doi.org/10.1007/978-3-642-12304-7_48" @default.
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