Embedded bayesian network for pattern recognition

• US 7,203,368 B2
• Filed: 01/06/2003
• Issued: 04/10/2007
• Est. Priority Date: 01/06/2003
• Status: Expired due to Fees

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Nefian, Ara V.
• Primary Examiner(s): Mancuso; Joseph
• Assistant Examiner(s): DULANEY, KATHLEEN YUAN
• Application Number:
  US10/269,381
• Publication Number:
  US 20040131259A1
• Time in Patent Office: 1,555 Days
• Field of Search: 382/228, 382/159, 700/47, 704/256.1
• 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 pa 20 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 (HMM) and a coupled hidden Markov model (CHMM), 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;

wherein either the parent layer is formed of an HMM and the child layer is formed of a CHMM, or the parent layer is formed of a CHMM and the child layer is formed of an HMM;

the hierarchical statistical model 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;

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

obtaining an observation vector sequence from a pattern to be recognized; and

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

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

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

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

5. The method according to claim 1, wherein identifying the object comprises using a Viterbi algorithm to determine a substantially optimal state sequence for the hierarchical statistical model based on the observation vector sequence.

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

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

forming a hierarchical statistical model using a hidden Markov model (HMM) and a coupled hidden Markov model (CHMM), 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;

wherein either the parent layer is formed of an HMM and the child layer is formed of a CHMM, or the parent layer is formed of a CHMM and the child layer is formed of an HMM;

the hierarchical statistical model 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;

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

obtaining an observation vector sequence from a pattern to be recognized; and

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

8. The article of claim 7, wherein the hierarchical statistical model defines an initial super state probability in a super channel.

9. The article of claim 7, wherein the hierarchical statistical model defines super state transition probability from a sequence of states in the super channel.

10. The article of claim 7, wherein the hierarchical statistical model defines observation likelihood given a state of a channel.

11. The article of claim 7, wherein identifying the object comprises using a Viterbi algorithm to determine a substantially optimal state sequence for the hierarchical statistical model based on the observation vector sequence.

12. The article of claim 7, wherein the data set includes two-dimensional data.

13. A system comprising:

a hierarchical statistical model to use both hidden Markov models and coupled hidden Markov models to model patterns, 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;

wherein either the parent layer is formed of a hidden Markov model (HMM) and the child layer is formed of a coupled HMM (CHMM), or the parent layer is formed of a CHMM and the child layer is formed of an HMM;

the hierarchical statistical model 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;

a training module to train for the hierarchical statistical model using observation vectors extracted from a data set; and

an identification module to obtain an observation vector sequence for a pattern to be recognized, and to identify the pattern by finding a substantially optimal state sequence segmentation for the hierarchical statistical model.

14. The system according to claim 13, wherein the hierarchical statistical model defines an initial super state probability in a super channel.

15. The system according to claim 13, wherein the hierarchical statistical model defines super state transition probability from a sequence of states in the super channel.

16. The system according to claim 13, wherein the hierarchical statistical model defines observation likelihood given a state of a channel.

17. The system according to claim 13, wherein the identification module is adapted to use a Viterbi algorithm to determine a substantially optimal state sequence for the hierarchical statistical model based on the observation vector sequence.

18. The system according to claim 13, wherein the data set includes two-dimensional data.