Method of using temporal context for image classification

• US 20050105775A1
• Filed: 11/13/2003
• Published: 05/19/2005
• Est. Priority Date: 11/13/2003
• Status: Active Grant

• Current Assignee: Monument Peak Ventures, LLC (Dominion Harbor Enterprises, LLC)
• Original Assignee: Eastman Kodak Company
• Inventors: Luo, Jiebo, Boutell, Matthew R.
• Granted Patent: US 7,680,340 B2
• Time in Patent Office: Days
• US Class Current: 382/115
• CPC Class Codes: G06F 16/583 using metadata automatically derived from the content
• CPC Class Codes: G06F 18/295 Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
• CPC Class Codes: G06V 10/85 Markov-related models; Markov random fields
• 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

Abstract

A method improving scene classification of a sequence of digital images comprising the steps of: (a) providing a sequence of images captured in temporal succession; (b) classifying each of the images individually based on information contained in the image alone to generate a first image classification; and (c) imposing a pre-determined temporal context model on the sequence of images to generate a final image classification for each image in the sequence.

Claims

1. A method for improving scene classification of a sequence of digital images comprising the steps of:

(a) providing a sequence of images captured in temporal succession;

(b) classifying each of the images individually based on information contained in the individual image to generate a first image classification; and

(c) imposing a pre-determined temporal context model on the sequence of images to generate a final image classification.

2. The method as claimed in claim 1 wherein the information used in step (b) includes pixel information.

3. The method as claimed in claim 1 wherein the information used in step (b) includes metadata information.

4. The method as claimed in claim 1 wherein the pre-determined temporal context model in step (c) is independent of elapsed time between consecutive images

5. The method as claimed in claim 1 wherein the pre-determined temporal context model in step (c) is dependent on elapsed time between consecutive images

6. The method as claimed in claim 1 wherein the pre-determined temporal context model is a causal Hidden Markov Model dependent on the previous image.

7. The method as claimed in claim 6 wherein the pre-determined temporal context model is a causal Hidden Markov Model dependent on the previous image.

8. The method as claimed in claim 1 wherein the pre-determined temporal context model is a non-casual model dependent on both the previous and subsequent images.

9. The method as claimed in claim 8 wherein the pre-determined temporal context model is a non-casual model dependent on both the previous and subsequent images.

10. The method as claimed in claim 1 wherein the temporal context model is imposed using Viterbi algorithm.

11. The method as claimed in claim 1 wherein the temporal context model is imposed using the belief propagation algorithm.