ASSIGNING LABELS TO IMAGES IN A COLLECTION

• US 20100226582A1
• Filed: 03/03/2009
• Published: 09/09/2010
• Est. Priority Date: 03/03/2009
• Status: Abandoned Application

• Current Assignee: Eastman Kodak Company
• Original Assignee: Eastman Kodak Company
• Inventors: Cao, Liangliang, Luo, Jiebo
• Application Number:
  US12/396,642
• Publication Number:
  US 20100226582A1
• Time in Patent Office: Days
• US Class Current: 382/224
• CPC Class Codes: G06F 16/58 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 18/2321 using statistics or function optimisation, e.g. modelling of probability density functions
• CPC Class Codes: G06V 10/763 Non-hierarchical techniques, e.g. based on statistics of modelling distributions
• CPC Class Codes: G06V 20/38 Outdoor scenes
• CPC Class Codes: G06V 20/39 Urban scenes

Abstract

A method of assigning semantic labels to images in a particular collection, includes acquiring seed labels for a subset of images; propagating the seed labels to other images according to a similarity metric; and storing the semantic labels, including both seed labels and propagated labels with the corresponding images.

Claims

1. A method of assigning semantic labels to images in a particular collection, comprising:

(a) acquiring seed labels for a subset of images;

(b) propagating the seed labels to other images according to a similarity metric; and

(c) storing the semantic labels, including both seed labels and propagated labels, with the corresponding images.

2. The method of claim 1 wherein the seed labels are acquired at least in part from a user.

3. The method of claim 1 wherein the similarity metric includes visual similarity or metadata similarity, or combinations thereof.

4. The method of claim 3 wherein the visual similarity is computed based on color histogram, or SIFT features, or combinations thereof.

5. The method of claim 3 wherein the metadata similarity is computed based on timestamp, or GPS coordinates, or combinations thereof.

6. The method of claim 1 wherein the stored semantic labels are used for searching or organizing images or image collections.

7. The method of claim 1 wherein the semantic label is either positive or negative evidence.

8. The method of claim 1 wherein the label propagation step comprises:

(i) estimating the probability of label propagation from one photo to another using a correlation probability;

(ii) updating each unlabeled photo with respect to its probability by considering label probability of the other photos which are similar by a similarity measure; and

(iii) repeating this procedure until it converges, or reaches a predetermined maximum number of iterations.

9. A method of assigning semantic labels to images in a particular collection, comprising:

(a) analyzing the images in the collection using a set of predetermined semantic label classifiers to produce semantic labels with associated confidence values for each semantic label for each image;

(b) retaining only semantic labels for each image with confidence above a selected value as seed labels and discarding remaining semantic labels;

(c) propagating the seed labels to other images according to a similarity metric; and

(d) storing the semantic labels, including both seed labels and propagated labels, and the corresponding images.

10. The method of claim 9 wherein the seed labels are acquired at least in part from a user.

11. The method of claim 9 wherein the similarity metric includes visual similarity or metadata similarity, or combinations thereof.

12. The method of claim 11 wherein the visual similarity is computed based on color histogram, or SIFT features, or combinations thereof.

13. The method of claim 11 wherein the metadata similarity is computed based on timestamp, or GPS coordinates, or combinations thereof.

14. The method of claim 9 wherein the stored semantic labels are used for searching or organizing images or image collections.

15. The method of claim 9 wherein the semantic label is either positive or negative evidence.

16. The method of claim 9 wherein the label propagation step comprises:

(i) estimating the probability of label propagation from one photo to another using a correlation probability;

(ii) updating each unlabeled photo with respect to its probability by considering label probability of the other photos which are similar by a similarity measure; and

(iii) repeating this procedure until it converges, or reaches a predetermined maximum number of iterations.