Method and apparatus for determining a classification boundary for an object classifier

• US 8,041,115 B2
• Filed: 11/14/2007
• Issued: 10/18/2011
• Est. Priority Date: 11/17/2006
• Status: Expired due to Fees

• Current Assignee: Alpine Electronics Incorporated (Alps Alpine Co., Ltd.)
• Original Assignee: Alpine Electronics Incorporated (Alps Alpine Co., Ltd.)
• Inventors: Liu, Wei, Yuan, Huai
• Primary Examiner(s): Mehta; Bhavesh
• Assistant Examiner(s): Krasnic; Bernard
• Application Number:
  US11/940,063
• Publication Number:
  US 20080118161A1
• Time in Patent Office: 1,434 Days
• Field of Search: 382/103, 382/159, 382/173, 382/180, 382/181, 382/190, 382/199, 382/224
• US Class Current: 382/173
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06V 2201/08 Detecting or categorising vehicles

Abstract

In a method and apparatus for determining a classification boundary between an object, such as a vehicle, and a background, using an object classifier, a trained classifier is configured to classify and recognize each of a plurality of object images and a plurality of background images. Next, a confidence probability density distribution function is calculated for the vehicle and the background using the determined confidence values for the vehicle images and background images. Once the probability density distribution functions for the vehicle and the background are calculated, the classification boundary between the vehicle and the background is determined using the probability density distribution functions for the vehicle or the background, or both, and a predefined model that is appropriate.

Claims

1. A method for determining a classification boundary between an object and a background, comprising:

recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images;

classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images;

determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images;

calculating a confidence probability density distribution function for an object in the plurality of object images, wherein the confidence probability density distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images;

calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and

determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for both the object in the plurality of object images and the background in the plurality of background images;

wherein the predefined model comprises a probability of the object in the plurality of object images and the background in the plurality of background images being incorrectly classified, and the probability meets a predetermined target value; and

wherein the probability of the object in the plurality of object images and the background in the plurality of background images being incorrectly classified that meets the predetermined target value, is calculated by a formula;

min(α

∫

_T^∞

f_b(x)dx+b∫

<sub>−

∞</sub>

^Tf_v(x)dx),

wherein min( ) represents a minimization operation, f_v(x) is the confidence probability density distribution function for the object in the plurality of object images, f_b(x) is the confidence probability density distribution function for the background in the plurality of background images, a represents a penalty factor for incorrectly recognizing the background in the plurality of background images, b represents a penalty factor for incorrectly recognizing the object in the plurality of object images, and T represents the classification boundary.

2. The method of claim 1, wherein b is greater than a.

3. The method of claim 1, wherein the confidence probability density distribution functions for the object and the background are normally distributed.

4. A method for determining a classification boundary between an object and a background, comprising:

recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images;

classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images;

determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images;

calculating a confidence probability density distribution function for an object in the plurality of object images, wherein the confidence probability density distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images;

calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and

determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for both the object in the plurality of object images and the background in the plurality of background images;

wherein the predefined model comprises a probability of correctly recognizing the object in the plurality of object images, and the probability meets a predetermined target value; and

wherein the probability of correctly recognizing the object in the plurality of object images that meets the predetermined target value, is calculated by a formula;

5. A method for determining a classification boundary between an object and a background, comprising:

recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images;

classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images;

determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images;

calculating a confidence probability density distribution function for an object in the plurality of object images, wherein the confidence probability density distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images;

calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and

determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for both the object in the plurality of object images and the background in the plurality of background images;

wherein the predefined model comprises a probability of incorrectly recognizing the object in the plurality of object images, and the probability meets a predetermined target value; and

wherein the probability of incorrectly recognizing the object in the plurality of object images that meets the predetermined target value, is calculated by a formula;

6. A method for determining a classification boundary between an object and a background, comprising:

recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images;

classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images;

determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images;

calculating a confidence probability density distribution function for an object in the plurality of object images, wherein the confidence probability density distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images;

calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and

determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for both the object in the plurality of object images and the background in the plurality of background images;

wherein the predefined model comprises a probability of correctly recognizing, in a new plurality of images, the object in the plurality of object images, and the probability meets a predetermined target value; and

wherein the probability of correctly recognizing, in the new plurality of images, the object in the plurality of object images that meets the predetermined target value, is calculated by a formula;

7. A method for determining a classification boundary between an object and a background, comprising:

recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images;

classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images;

determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images;

calculating a confidence probability density distribution function for an object in the plurality of object images, wherein the confidence probability density distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images;

calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and

determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for both the object in the plurality of object images and the background in the plurality of background images;

wherein the predefined model comprises a probability of incorrectly recognizing, in a new plurality of images, the background in the plurality of background images, and the probability meets a predetermined target value; and

wherein the probability of incorrectly recognizing, in the new plurality of images, the background in the plurality of background images that meets the predetermined target value, is calculated by a formula;

8. An apparatus for determining a classification boundary between an object and a background, comprising:

a recognizing unit, using a computer processor configured to classify and recognize each of a plurality of object images and background images, using a trained classifier, in order to determine a confidence value for each of the plurality of object images and background images;

a calculating unit, using a computer processor configured to calculate confidence probability density distribution functions for (1) an object in the plurality of object images and (2) a background in the plurality of background images, wherein the calculation of the confidence probability density distribution function for the object in the plurality of object images is based on the confidence values determined for each object image, and the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for each background in the plurality of background images; and

a determining unit, using a computer processor configured to determine a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for both the object in the plurality of object images and the background in the plurality of background images;

wherein the predefined model comprises one of;

(a) a probability of the object in the plurality of object images and the background in the plurality of background images being incorrectly classified, and the probability meets a predetermined target value, wherein the probability of the object in the plurality of object images and the background in the plurality of background images being incorrectly classified that meets the predetermined target value is calculated by a formula;

9. A computer readable non-transitory medium storing computer executable instructions for causing a computer processor to perform the steps of:

recognizing, using a trained classifier, each of a plurality of object images and each of a plurality of background images;

classifying, using the trained classifier, each of the plurality of object images and each of the plurality of background images;

determining a confidence value for each of the plurality of recognized and classified object images and for each of the plurality of recognized and classified background images;

calculating a confidence probability density distribution function for an object in the plurality of object images, wherein the confidence probability density distribution function for the object in the plurality of object images is based on the confidence values determined for the plurality of object images;

calculating a confidence probability density distribution function for a background in the plurality of background images, wherein the confidence probability density distribution function for the background in the plurality of background images is based on the confidence values determined for the plurality of background images; and

determining a classification boundary between the object in the plurality of object images and the background in the plurality of background images using a predefined model, wherein the predefined model is based on the calculated confidence probability density distribution functions for the object in the plurality of object images or the background in the plurality of background images, or the calculated confidence probability density distribution functions for both the object in the plurality of object images and the background in the plurality of background images;

wherein the predefined model comprises one of;

(a) a probability of the object in the plurality of object images and the background in the plurality of background images being incorrectly classified, and the probability meets a predetermined target value, wherein the probability of the object in the plurality of object images and the background in the plurality of background images being incorrectly classified that meets the predetermined target value is calculated by a formula;