Embedded bayesian network for pattern recognition

• US 20040131259A1
• Filed: 01/06/2003
• Published: 07/08/2004
• Est. Priority Date: 01/06/2003
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Nefian, Ara V.
• Granted Patent: US 7,203,368 B2
• Time in Patent Office: Days
• US Class Current: 382/228
• CPC Class Codes: G06F 18/29 Graphical models, e.g. Bayesian networks

Abstract

A pattern recognition procedure forms a hierarchical statistical model using a hidden Markov model and a coupled hidden Markov model. The hierarchical statistical model supports a pa20 layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer. After training, the hierarchical statistical model uses observation vectors extracted from a data set to find a substantially optimal state sequence segmentation.

Claims

1. A pattern recognition method, comprising:

forming a hierarchical statistical model using a hidden Markov model and a coupled hidden Markov model, the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer,

training the hierarchical statistical model using observation vectors extracted from a data set, and

finding a substantially optimal state sequence segmentation for the hierarchical statistical model.

2. The method according to claim 1, wherein the parent layer is formed from the hidden Markov model and the child layer is formed from the coupled hidden Markov model.

3. The method according to claim 1, wherein the parent layer is formed from the coupled hidden Markov models and the child layer is formed from the hidden Markov models.

4. The method according to claim 1, wherein the hierarchical statistical model is applied to two dimensional data, with the parent layer describing data in a first direction and the child layer describing data in a second direction orthogonal to the first direction.

5. The method according to claim 1, wherein the hierarchical statistical model defines an initial super state probability in a super channel.

6. The method according to claim 1, wherein the hierarchical statistical model defines super state transition probability from a sequence of states in the supper channel.

7. The method according to claim 1, wherein the hierarchical statistical model defines observation likelihood given a state of a channel.

8. The method according to claim 1, wherein training further comprises collecting the training samples;

and training the hierarchical statistical model using the training samples.

9. The method according to claim 1, wherein the optimal state sequence segmentation is the Viterbi algorithm for the embedded coupled hidden Markov model/hidden Markov model.

10. The method according to claim 1, wherein the optimal state sequence segmentation is the Viterbi algorithm for the embedded hidden Markov model/coupledhidden Markov model

11. The method according to claim 1, wherein the data set includes two-dimensional data.

37. The method according to claim 1, wherein the optimal state sequence segmentation is the Viterbi algorithm for the embedded coupled hidden Markov model/hidden Markov model.

12. A pattern recognition method, comprising:

forming a hierarchical statistical model using a hidden Markov model and a coupled hidden Markov model, the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer, and

finding a substantially optimal state sequence segmentation for the hierarchical statistical model using a Viterbi based algorithm.

13. The method according to claim 12, wherein the parent layer is formed from the hidden Markov model and the child layer is formed from the coupled hidden Markov model.

14. The method according to claim 12, wherein the parent layer is formed from the coupled hidden Markov models and the child layer is formed from the hidden Markov models.

15. An article comprising a storage medium having stored thereon instructions that when executed by a machine result in:

forming a hierarchical statistical model using a hidden Markov model and a coupled hidden Markov model, the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer,

training the hierarchical statistical model using observation vectors extracted from a data set, and

finding a substantially optimal state sequence segmentation for the hierarchical statistical model.

16. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the parent layer is formed from the hidden Markov model and the child layer is formed from the coupled hidden Markov model.

17. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the parent layer is formed from the coupled hidden Markov models and the child layer is formed from the hidden Markov models.

18. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the hierarchical statistical model is applied to two dimensional data, with the parent layer describing data in a first direction and the child layer describing data in a second direction orthogonal to the first direction.

19. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the hierarchical statistical model defines an initial super state probability in a super channel.

20. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the hierarchical statistical model defines super state transition probability from a sequence of states in the supper channel.

21. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the hierarchical statistical model defines observation likelihood given a state of a channel.

22. The article comprising a storage medium having stored thereon instructions of claim 15, wherein training further comprises collecting the training samples;

and training the hierarchical statistical model using the training samples.

23. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the optimal state sequence segmentation is the Viterbi algorithm for the embedded coupled hidden Markov model/hidden Markov model.

24. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the optimal state sequence segmentation is the Viterbi algorithm for the embedded hidden Markov model/coupledhidden Markov model

25. The article comprising a storage medium having stored thereon instructions of claim 15, wherein the data set includes two-dimensional data.

26. An article comprising a storage medium having stored thereon instructions that when executed by a machine result in:

forming a hierarchical statistical model using a hidden Markov model and a coupled hidden Markov model, the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer, and

finding a substantially optimal state sequence segmentation for the hierarchical statistical model using a Viterbi based algorithm.

27. The article comprising a storage medium having stored thereon instructions of claim 26, wherein the parent layer is formed from the hidden Markov model and the child layer is formed from the coupled hidden Markov model.

28. The article comprising a storage medium having stored thereon instructions of claim 26, wherein the parent layer is formed from the coupled hidden Markov models and the child layer is formed from the hidden Markov models.

29. A system comprising:

a hierarchical statistical model using both hidden Markov models and coupled hidden Markov models, the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer,

a training module for the hierarchical statistical model that uses observation vectors extracted from a data set, and

an identification module for the hierarchical statistical model that finds a substantially optimal state sequence segmentation.

30. The system according to claim 29, wherein the parent layer is formed from the hidden Markov model and the child layer is formed from the coupled hidden Markov model.

31. The system according to claim 29, wherein the parent layer is formed from the coupled hidden Markov models and the child layer is formed from the hidden Markov models.

32. The system according to claim 29, wherein the hierarchical statistical model is applied to two dimensional data, with the parent layer describing data in a first direction and the child layer describing data in a second direction orthogonal to the first direction.

33. The system according to claim 29, wherein the hierarchical statistical model defines an initial super state probability in a super channel.

34. The system according to claim 29, wherein the hierarchical statistical model defines super state transition probability from a sequence of states in the supper channel.

35. The system according to claim 29, wherein the hierarchical statistical model defines observation likelihood given a state of a channel.

36. The system according to claim 29, wherein training further comprises collecting the training samples;

and training the hierarchical statistical model using the training samples.

38. The system according to claim 29, wherein the optimal state sequence segmentation is the Viterbi algorithm for the embedded hidden Markov model/coupledhidden Markov model

39. The system according to claim 29, wherein the data set includes two-dimensional data.

40. A pattern recognition system, comprising:

a hierarchical statistical model using a hidden Markov model and a coupled hidden Markov model, the hierarchical statistical model supporting a parent layer having multiple supernodes and a child layer having multiple nodes associated with each supernode of the parent layer, and

an identification module for the hierarchical statistical model that finds a substantially optimal state sequence segmentation, using a Viterbi based algorithm.

41. The method according to claim 40, wherein the parent layer is formed from the hidden Markov model and the child layer is formed from the coupled hidden Markov model.

42. The method according to claim 40, wherein the parent layer is formed from the coupled hidden Markov models and the child layer is formed from the hidden Markov models.