Continuous interaction learning and detection in real-time

Patent metadata

Field	Value
Publication number	US-9390380-B2
Application number	US-201313976744-A
Country	US
Kind code	B2
Filing date	Mar 15, 2013
Priority date	Mar 15, 2013
Publication date	Jul 12, 2016
Grant date	Jul 12, 2016

Abstract

Systems and methods may provide for partitioning a plurality of training samples into a first sequential list of centroids, removing one or more repeating centroids in the first sequential list of centroids to obtain a first reduced list of centroids and generating a set of Hidden Markov Model (HMM) parameters based on the first reduced list of centroids. Additionally, a plurality of detection samples may be partitioned into a second sequential list of centroids, wherein one or more repeating centroids in the second sequential list of centroids may be removed to obtain a second reduced list of centroids. The second reduced list of centroids may be used to determine a match probability for the plurality of detection samples against the set of HMM parameters. In one example, the reduced lists of centroids lack temporal variability.

First claim

We claim:

1. An apparatus to process training samples, comprising:
a computing device including a sensor to collect a plurality of training samples, the computing device including:
a partition module to partition the plurality of training samples into a sequential list of centroids;
a filter to remove one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and
a parameter module to generate a set of Hidden Markov Model (HMM) parameters based on the reduced list of centroids and to apply a sliding window to the reduced list of centroids, wherein the sliding window has a fixed width that equals a number of clusters in a set of clusters associated with the sequential list of centroids.

2. The apparatus of claim 1 , wherein the reduced list of centroids is to lack temporal variability.

3. The apparatus of claim 1 , wherein the sequential list of centroids is to be associated with a set of clusters having a one-to-one mapping to states in the HMM.

4. The apparatus of claim 1 , wherein the partition module includes:
a cluster unit to determine a set of clusters for the plurality of training samples;
a centroid unit to determine a set of cluster centroids corresponding to the set of clusters; and
an assignment unit to assign each training sample in the plurality of training samples to a centroid in the set of cluster centroids.

5. The apparatus of claim 1 , wherein the partition module is to partition a plurality of gesture samples into the sequential list of centroids.

6. The apparatus of claim 1 , wherein the partition module is to partition a plurality of speech samples into the sequential list of centroids.

7. A method to process training samples, comprising:
partitioning a plurality of training samples into a sequential list of centroids;
removing one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and
generating a set of Hidden Markov Model (HMM) parameters based on the reduced list of centroids;
applying a sliding window to the reduced list of centroids, wherein the sliding window has a fixed width that equals a number of clusters in a set of clusters associated with the sequential list of centroids.

8. The method of claim 7 , wherein the reduced list of centroids lacks temporal variability.

9. The method of claim 7 , wherein sequential list of centroids is associated with a set of clusters having a one-to-one mapping to states in the HMM.

10. The method of claim 7 , wherein partitioning the plurality of training samples includes:
determining a set of clusters for the plurality of training samples;
determining a set of cluster centroids corresponding to the set of clusters; and
assigning each training sample in the plurality of training samples to a centroid in the set of cluster centroids.

11. The method of claim 7 , wherein a plurality of gesture samples are partitioned into the sequential list of centroids.

12. The method of claim 7 , wherein a plurality of speech samples are partitioned into the sequential list of centroids.

13. A method to process detection samples, comprising:
partitioning a plurality of detection samples into a sequential list of centroids;
removing one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and
using the reduced list of centroids to determine a match probability for the plurality of detection samples against a set of Hidden Markov Model (HMM) parameters associated with a training session including applying a sliding window to the reduced list of centroids, and wherein the sliding window has a fixed width that equals a number of clusters in a set of clusters associated with the sequential list of centroids.

14. The method of claim 13 , wherein the reduced list of centroids lacks temporal variability.

15. The method of claim 13 , wherein the sequential list of centroids is associated with a set of clusters having a one-to-one mapping to states in the HMM.

16. The method of claim 13 , wherein a plurality of gesture samples are partitioned into the sequential list of centroids.

17. The method of claim 13 , wherein a plurality of speech samples are partitioned into the sequential list of centroids.

18. At least one non-transitory computer readable storage medium comprising a set of instructions which, if executed by a computing device, cause the computing device to:
partition a plurality of detection samples into a sequential list of centroids;
remove one or more repeating centroids in the sequential list of centroids to obtain a reduced list of centroids; and
use the reduced list of centroids to determine a match probability for the plurality of detection samples against a set of Hidden Markov Model (HMM) parameters associated with a training session; and
apply a sliding window to the reduced list of centroids to use the list to determine the match probability, and wherein the sliding window is to have a fixed width that equals a number of clusters in a set of clusters associated with the sequential list of centroids.

19. The at least one computer readable storage medium of claim 18 , wherein the reduced list of centroids is to lack temporal variability.

20. The at least one computer readable storage medium of claim 18 , wherein the sequential list of centroids is to be associated with a set of clusters having a one-to-one mapping to states in the HMM.

21. The at least one computer readable storage medium of claim 18 , wherein a plurality of gesture samples are to be partitioned into the sequential list of centroids.

22. The at least one computer readable storage medium of claim 18 , wherein a plurality of speech samples are to be partitioned into the sequential list of centroids.

Assignees

• Intel Corp

Inventors

• Evans Chuck

Classifications

• G06F18/295

  Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06F18/24137 Primary

  Distances to cluster centroïds · CPC title

• G06N20/00 Primary

  Machine learning · CPC title