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Statistical image modeling for semantic segmentation

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3 Author(s)
Zhongjie Zhu ; Ningbo Key Lab. of DSP, Zhejiang Wanli Univ., Ningbo, China ; Yuer Wang ; Gangyi Jiang

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.

Published in:

Consumer Electronics, IEEE Transactions on  (Volume:56 ,  Issue: 2 )

Date of Publication:

May 2010

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