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Statistical Image Modeling for Semantic Segmentation
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DOI：10.1109/TCE.2010.5506001.png)
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摘要
En 中文
Semantic image segmentation (SIS) is one of the most crucial steps toward image understanding. In this paper, a novel framework to enable SIS is proposed by modeling images automatically. The statistical model for an image is automatically obtained by using a finite mixture model to approximate the underlying class distributions of image pixels. To accurately characterize the principal visual properties of the underlying dominant image compounds, a novel improved Expectation-Maximization (EM) algorithm is presented to select model structure and estimate model parameters simultaneously. Experiments were conducted and convincing results are obtained(1).
Keyword:
Statistical image modeling
semantic image segmentation
image understanding
finite mixture model
improved EM algorithm
期刊
IF:
10.9
论文数:
5.3K
被引数:
6.8K
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