Scene Classification Apparatus and Scene Classification Method

• US 20080279456A1
• Filed: 05/07/2008
• Published: 11/13/2008
• Est. Priority Date: 05/08/2007
• Status: Abandoned Application

• Current Assignee: Seiko Epson Corporation (Seiko Group)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: KASAHARA, Hirokazu, SATO, Kaori, KASAI, Tsuneo
• Application Number:
  US12/116,817
• Publication Number:
  US 20080279456A1
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06F 18/2415 based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 30/242 Division of the character sequences into groups prior to recognition; Selection of dictionaries

Abstract

The present invention is provided with: a characteristic amount obtaining section that obtains a partial characteristic amount indicating a characteristic of a partial image that constitutes a part of a classification target image; a partial evaluation section that carries out an evaluation based on the partial characteristic amount obtained by the characteristic amount obtaining section as to whether or not the partial image pertains to a specific scene; and a determining section that determines whether or not the classification target image pertains to the specific scene by using an evaluation result of the partial evaluation section for only the partial images corresponding respectively to a predetermined M number of partial areas among an N number of the partial areas (M<N) obtained by dividing an image overall area.

Claims

1. A scene classification apparatus, comprising:

(A) a characteristic amount obtaining section that obtains a partial characteristic amount indicating a characteristic of a partial image that constitutes a part of a classification target image;

(B) a partial evaluation section that carries out an evaluation based on the partial characteristic amount obtained by the characteristic amount obtaining section as to whether or not the partial image pertains to a specific scene; and

(C) a determining section that determines whether or not the classification target image pertains to the specific scene by using an evaluation result of the partial evaluation section for only the partial images corresponding respectively to a predetermined M number of partial areas among an N number of the partial areas (M<

N) obtained by dividing an image overall area.

2. A scene classification apparatus according to claim 1,

wherein the M value is determined based on

a precision that is a probability that, when it has been determined with the determining section that the classification target image pertains to the specific scene, the determination thereof is correct, and

a recall that is a probability that the classification target image pertaining to the specific scene is to be determined with the determining section to pertain to the specific scene.

3. A scene classification apparatus according to claim 1,

wherein the M number of the partial areas are selected from the N number of the partial areas based on at least one of

an existence probability that is a probability that a characteristic of the specific scene is expressed in the partial area, and

a partial precision that is a probability that, when an evaluation result indicating that the partial image pertains to the specific scene has been obtained by the partial evaluation section, the evaluation result thereof is correct.

4. A scene classification apparatus according to claim 1,

wherein the determining section determines that,

when the number of the partial images for which an evaluation result has been obtained indicating that the partial images pertain to the specific scene has exceeded a predetermined threshold,

the classification target image pertains to the specific scene.

5. A scene classification apparatus according to claim 4,

wherein the determining section

determines that the classification target image does not pertain to the specific scene when an addition value of:

the number of the partial images for which an evaluation result, indicating that the partial images pertain to the specific scene, has been obtained; and

the number of the partial images, among the M number of the partial images, for which an evaluation has not been carried out by the partial evaluation section, has not reached the predetermined threshold.

6. A scene classification apparatus according to claim 1,

wherein provided with the partial evaluation section for each type of the specific scene that is a classification target.

7. A scene classification apparatus according to claim 6,

wherein the M value

is established for each type of the specific scene based on the precision and the recall of the specific scene.

8. A scene classification apparatus according to claim 6,

wherein the determining section determines that,

when the number of the partial images for which an evaluation result, indicating that the partial images pertain to the specific scene, has been obtained has exceeded a predetermined threshold,

the classification target image pertains to the specific scene, and

the predetermined threshold

is set for a plurality of the specific scenes respectively.

9. A scene classification apparatus according to claim 6,

wherein the determining section,

when unable to determine that the classification target image pertains to a certain specific scene by using an evaluation result of a certain partial evaluation section, determines whether or not the classification target image pertains to another specific scene by using an evaluation result of another partial evaluation section.

10. A scene classification apparatus according to claim 1,

wherein the characteristic amount obtaining section

further obtains an overall characteristic amount indicating a characteristic of the classification target image, and

the partial evaluation section

evaluates based on the partial characteristic amount and the overall characteristic amount whether or not the partial image pertains to the specific scene.

11. A scene classification method, comprising:

(A) obtaining a partial characteristic amount indicating a characteristic of a partial image that constitutes a part of a classification target image;

(B) carrying out an evaluation based on the partial characteristic amount as to whether or not the partial image pertains to a specific scene; and

(C) determining whether or not the classification target image pertains to the specific scene by using an evaluation result for only the partial images corresponding respectively to a predetermined M number of partial areas among an N number of the partial areas (M<

N) obtained by dividing an image overall area.

12. A scene classification method according to claim 11, comprising:

determining the M value based on

a precision that is a probability, when a determination has been performed that the classification target image pertains to the specific scene, that the determination thereof is correct, and

a recall that is a probability that the classification target image pertaining to the specific scene is to be determined to pertain to the specific scene.

13. A scene classification method according to claim 12, comprising:

determining as the number of provisional evaluation an M′

number (M′

<

N) of the partial images among the partial images corresponding respectively to the N number of the partial areas in a sample image;

obtaining the precision and the recall for each of the thresholds by setting a plurality of thresholds equal to or less than the M′

number as thresholds for the number of the partial images for which an evaluation result that the partial image pertains to the specific scene has been obtained, which are for determining whether or not the sample image pertains to the specific scene;

obtaining a maximum function value in the number of the provisional evaluation by calculating a function value prescribed by the precision and the recall for each of the thresholds; and

determining as the M value the M′

value of when the maximum function value among the maximum function values obtained with the number of the provisional evaluation becomes largest when the M′

value has been varied within a range equal to or less than the N number.