INFORMATION PROCESSING APPARATUS, CLUSTERING METHOD, AND RECORDING MEDIUM STORING CLUSTERING PROGRAM

• US 20140149412A1
• Filed: 10/28/2013
• Published: 05/29/2014
• Est. Priority Date: 11/26/2012
• Status: Active Grant

• Current Assignee: Ricoh Company Limited
• Original Assignee: Ricoh Company Limited
• Inventors: NAKAMURA, Satoshi
• Granted Patent: US 9,519,660 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/583 using metadata automatically derived from the content
• CPC Class Codes: G06F 18/23213 with fixed number of clusters, e.g. K-means clustering
• CPC Class Codes: G06F 18/24137 Distances to cluster centroïds
• CPC Class Codes: G06V 10/763 Non-hierarchical techniques, e.g. based on statistics of modelling distributions
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/35 Categorising the entire scene, e.g. birthday party or wedding scene

Abstract

An information processing apparatus, a clustering method, and a clustering program stored on a recording medium, each of which determines an initial value of model parameter of an input data set based on a model parameter of a reference data set that is similar to the input data set and is previously clustered, modifies the initial value so as to match the input data set, and to obtain a clustering result of the input data set using the updated initial value of model parameter.

Claims

1. An information processing apparatus that clusters an input data set, comprising:

a memory to store, for each of a plurality of reference data sets that is previously clustered, a reference parameter used for clustering the reference data set, the reference parameter being a model parameter of mixture distribution; and

a processor to

search the memory to obtain the reference parameter of at least one reference data set that is similar to the input data set;

determine an initial value of a model parameter of mixture distribution of the input data set, based on the reference parameter of the at least one reference data set;

modify the initial value of the model parameter of mixture distribution of the input data set, so as to match a probability density distribution of the input data set to generate an updated initial value; and

cluster the probability density distribution of the input data set on a feature space of the input data set, using the updated initial value of the model parameter of mixture distribution.

2. The information processing apparatus of claim 1, wherein the processor obtains a plurality of reference parameters based on the degree of similarity between the input data set and the reference data set as the at least one reference data set that is similar to the input data set, and combines the plurality of reference parameters based on the degree of similarity to determine the initial value of the model parameter of mixture distribution of the input data set.

3. The information processing apparatus of claim 2, wherein the processor calculates the degree of similarity based on a feature of the input data set and a feature of the reference data set, and searches the at least one reference data set based on the degree of similarity.

4. The information processing apparatus of claim 3, wherein the feature used for calculating the degree of similarity between the input data set and the reference data set in searching the at least one reference data set, differs from a feature used for clustering the input data set.

5. The information processing apparatus of claim 4, wherein the feature used for calculating the degree of similarity between the input data set and the reference data set includes at least one of a feature indicating the shape of a data point in the data set, and a feature indicating the texture of the data point in the data set.

6. The information processing apparatus of claim 4, wherein the feature used for clustering the input data set includes a feature indicating the color of a data point in the data set.

7. The information processing apparatus of claim 1, wherein the model of mixture distribution is a Gaussian mixture distribution model.

8. The information processing apparatus of claim 1, wherein the input data set is an image, and each data point in the input data set is a pixel or a set of a plurality of pixels in the image.

9. A method of clustering an input data set, comprising:

storing, in a memory, for each of a plurality of reference data sets that is previously clustered, a reference parameter used for clustering the reference data set, the reference parameter being a model parameter of mixture distribution;

searching the memory to obtain the reference parameter of at least one reference data set that is similar to the input data set;

determining an initial value of a model parameter of mixture distribution of the input data set, based on the reference parameter of the at least one reference data set;

modifying the initial value of the model parameter of mixture distribution of the input data set, so as to match a probability density distribution of the input data set to generate an updated initial value; and

clustering the probability density distribution of the input data set on a feature space of the input data set, using the updated initial value of the model parameter of mixture distribution.

10. The method of claim 9, wherein, when a plurality of reference parameters is obtained based on the degree of similarity between the input data set and the reference data set as the at least one reference data set that is similar to the input data set, the method further comprises:

combining the plurality of reference parameters based on the degree of similarity to determine the initial value of the model parameter of mixture distribution of the input data set.

11. The method of claim 10, further comprising:

calculating the degree of similarity based on a feature of the input data set and a feature of the reference data set,

wherein the searching searches the at least one reference data set based on the degree of similarity.

12. The method of claim 11, wherein the feature used for calculating the degree of similarity between the input data set and the reference data set in searching the at least one reference data set, differs from a feature used for clustering the input data set.

13. The method of claim 12, wherein the feature used for calculating the degree of similarity between the input data set and the reference data set includes at least one of a feature indicating the shape of a data point in the data set, and a feature indicating the texture of the data point in the data set.

14. The method of claim 12, wherein the feature used for clustering the input data set includes a feature indicating the color of a data point in the data set.

15. The method of claim 9, wherein the model of mixture distribution is a Gaussian mixture distribution model.

16. The method of claim 9, wherein the input data set is an image, and each data point in the input data set is a pixel or a set of a plurality of pixels in the image.

17. A non-transitory recording medium storing a plurality of instructions which, when executed by a processor, cause the processor to perform a method of clustering an input data set, the method comprising:

storing, in a memory, for each of a plurality of reference data sets that is previously clustered, a reference parameter used for clustering the reference data set, the reference parameter being a model parameter of mixture distribution;

searching the memory to obtain the reference parameter of at least one reference data set that is similar to the input data set;

determining an initial value of a model parameter of mixture distribution of the input data set, based on the reference parameter of the at least one reference data set;

modifying the initial value of the model parameter of mixture distribution of the input data set, so as to match a probability density distribution of the input data set to generate an updated initial value; and

clustering the probability density distribution of the input data set on a feature space of the input data set, using the updated initial value of the model parameter of mixture distribution.