COMPUTER-READABLE MEDIUM STORING LEARNING-MODEL GENERATING PROGRAM, COMPUTER-READABLE MEDIUM STORING IMAGE-IDENTIFICATION-INFORMATION ADDING PROGRAM, LEARNING-MODEL GENERATING APPARATUS, IMAGE-IDENTIFICATION-INFORMATION ADDING APPARATUS, AND IMAGE-IDENTIFICATION-INFORMATION ADDING METHOD

• US 20120039527A1
• Filed: 03/03/2011
• Published: 02/16/2012
• Est. Priority Date: 08/11/2010
• Status: Abandoned Application

• Current Assignee: Fuji Xerox Company Limited (Fujifilm Holdings Corporation)
• Original Assignee: Fuji Xerox Company Limited (Fujifilm Holdings Corporation)
• Inventors: KATO, Noriji, FUKUI, Motofumi, QI, Wenyuan
• Application Number:
  US13/040,032
• Publication Number:
  US 20120039527A1
• Time in Patent Office: Days
• US Class Current: 382/159
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06V 10/464 using a plurality of salient features, e.g. bag-of-words [BoW] representations
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects

Abstract

A computer-readable medium storing a learning-model generating program causing a computer to execute a process is provided. The process includes: extracting feature values from an image for learning that is an image whose identification information items are already known, the identification information items representing the content of the image; generating learning models by using binary classifiers, the learning models being models for classifying the feature values and associating the identification information items and the feature values with each other; and optimizing the learning models for each of the identification information items by using a formula to obtain conditional probabilities, the formula being approximated with a sigmoid function, and optimizing parameters of the sigmoid function so that the estimation accuracy of the identification information items is increased.

Claims

1. A computer-readable medium storing a learning-model generating program causing a computer to execute a process, the process comprising:

extracting a plurality of feature values from an image for learning that is an image whose identification information items are already known, the identification information items representing the content of the image;

generating learning models by using a plurality of binary classifiers, the learning models being models for classifying the plurality of feature values and associating the identification information items and the plurality of feature values with each other; and

optimizing the learning models for each of the identification information items by using a formula to obtain conditional probabilities, the formula being approximated with a sigmoid function, and optimizing parameters of the sigmoid function so that the estimation accuracy of the identification information items is increased.

2. The computer-readable medium according to claim 1, wherein the optimizing includes using the same parameters of the sigmoid function for the same identification information item.

3. The computer-readable medium according to claim 1, wherein

the extracting extracts a plurality of kinds of feature values from the image for learning, and

the generating generates the learning models corresponding to each of the identification information items and corresponding to each of the plurality of kinds of feature values.

4. A computer-readable medium storing an image-identification-information adding program causing a computer to execute a process, the process comprising:

extracting a plurality of feature values from an image for learning that is an image whose identification information items are already known, the identification information items representing the content of the image;

generating learning models by using a plurality of binary classifiers, the learning models being models for classifying the plurality of feature values and associating the identification information items and the plurality of feature values with each other;

optimizing the learning models for each of the identification information items by using a formula to obtain conditional probabilities, the formula being approximated with a sigmoid function, and optimizing parameters of the sigmoid function so that the estimation accuracy of the identification information items is increased;

extracting a plurality of feature values from an object image; and

adding identification information items to the object image by using the plurality of extracted feature values and the optimized learning models.

5. The computer-readable medium according to claim 4, wherein the optimizing includes using the same parameters of the sigmoid function for the same identification information item.

6. The computer-readable medium according to claim 4, wherein

the extracting the plurality of feature values from the image for learning extracts a plurality of kinds of feature values from the image for learning, and

the generating generates the learning models corresponding to each of the identification information items and corresponding to each of the plurality of kinds of feature values.

7. A learning-model generating apparatus comprising:

a generating unit that extracts a plurality of feature values from an image for learning which is an image whose identification information items are already known, and that generates learning models by using binary classifiers, the learning models being models for classifying the plurality of feature values and associating the identification information items and the plurality of feature values with each other; and

an optimization unit that optimizes the learning models for each of the identification information items by using a formula to obtain conditional probabilities, the formula being approximated with a sigmoid function, and that optimizes parameters of the sigmoid function so that the estimation accuracy of the identification information items is increased.

8. The learning-model generating apparatus according to claim 7, wherein the optimization unit uses the same parameters of the sigmoid function for the same identification information item.

9. The learning-model generating apparatus according to claim 7, wherein the generating unit extracts a plurality of kinds of feature values from the image for learning, and generates the learning models corresponding to each of the identification information items and corresponding to each of the plurality of kinds of feature values.

10. An image-identification-information adding apparatus comprising:

a generating unit that extracts a plurality of feature values from an image for learning which is an image whose identification information items are already known, the identification information items representing the content of the image, and that generates learning models by using binary classifiers, the learning models being models for classifying the plurality of feature values and associating the identification information items and the plurality of feature values with each other;

an optimization unit that optimizes the learning models for each of the identification information items by using a formula to obtain conditional probabilities, the formula being approximated with a sigmoid function, and that optimizes parameters of the sigmoid function so that the estimation accuracy of the identification information items is increased;

a feature value extraction unit that extracts a plurality of feature values from an object image; and

an identification-information adding unit that adds identification information items to the object image using the plurality of feature values, which have been extracted by the feature value extraction unit, and using the learning models which have been optimized by the optimization unit.

11. The image-identification-information adding apparatus according to claim 10, wherein the optimization unit uses the same parameters of the sigmoid function for the same identification information item.

12. The image-identification-information adding apparatus according to claim 10, wherein the generating unit extracts a plurality of kinds of feature values from the image for learning, and generates the learning models corresponding to each of the identification information items and corresponding to each of the plurality of kinds of feature values.

13. An image-identification-information adding method comprising:

extracting a plurality of feature values from an image for learning that is an image whose identification information items are already known, the identification information items representing the content of the image;

generating learning models by using a plurality of binary classifiers, the learning models being models for classifying the plurality of feature values and associating the identification information items and the plurality of feature values with each other;

optimizing the learning models for each of the identification information items by using a formula to obtain conditional probabilities, the formula being approximated with a sigmoid function, and optimizing parameters of the sigmoid function so that the estimation accuracy of the identification information items is increased;

extracting a plurality of feature values from an object image; and

adding identification information items to the object image by using the plurality of extracted feature values and the optimized learning models.

14. The image-identification-information adding method according to claim 13, wherein the optimizing includes using the same parameters of the sigmoid function for the same identification information item.

15. The image-identification-information adding method according to claim 13, wherein

the extracting the plurality of feature values from the image for learning extracts a plurality of kinds of feature values from the image for learning, and

the generating generates the learning models corresponding to each of the identification information items and corresponding to each of the plurality of kinds of feature values.