1 / 15

Gaussian Mixture Model

Gaussian Mixture Model. Mixture Modeling. A formalism for modeling a probability density function as a sum of parameterized functions. Normal parameters. Number of hidden components. Class weights. Observations. Multivariate Normal. Class weight, class prior probability, multinomial.

kawena
Download Presentation

Gaussian Mixture Model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Gaussian Mixture Model

  2. Mixture Modeling A formalism for modeling a probability density function as a sum of parameterized functions. Normal parameters Number of hidden components Class weights Observations Multivariate Normal Class weight, class prior probability, multinomial Normal = Gaussian

  3. Gaussian Mixture

  4. Gaussian Mixture

  5. Data Likelihood Under the assumption that the pairs (Zi,Xi) are mutually independent, their joint density may be written

  6. Data Log Likelihood The complete-data log likelihood is thus

  7. EM Algorithm • E-Step: 估计Zim • M-Step: 估计πm, µm,∑m

  8. EM Algorithm: E-Step

  9. EM Algorithm: M-Step

  10. Bayesian Ying-Yang Learning • Proposed by Prof. Lei XU Reference: Jinwen Ma, Jianfeng Liu. The BYY annealing learning algorithm for Gaussian mixture with automated model selection, Pattern Recognition, 2007, 40:2029-2037.

  11. Fig. 6. The segmentation result on the color image of house. (a) The original color image of house; (b) the segmented image via the BYY annealing learning algorithm (after 21 iterations).

  12. Fig. 8. The segmentation result on the color image of jellies. • The original color image of jellies; • the segmented image via the BYY annealing learning algorithm (after 22 iterations).

More Related