METHOD AND APPARATUS FOR LOCALIZING AN OBJECT WITHIN AN IMAGE

• US 20120045132A1
• Filed: 08/23/2010
• Published: 02/23/2012
• Est. Priority Date: 08/23/2010
• Status: Abandoned Application

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Baqai, Farhan, Wong, Tak-Shing, Bae, Soo Hyun
• Application Number:
  US12/861,410
• Publication Number:
  US 20120045132A1
• Time in Patent Office: Days
• US Class Current: 382/195
• CPC Class Codes: G06V 10/464 using a plurality of salient features, e.g. bag-of-words [BoW] representations

Abstract

An improved method and apparatus for localizing objects within an image is disclosed. In one embodiment, the method comprises accessing at least one object model representing visual word distributions of at least one training object within training images, detecting whether an image comprises at least one object based on the at least one object model, identifying at least one region of the image that corresponds with the at least one detected object and is associated with a minimal dissimilarity between the visual word distribution of the at least one detected object and a visual word distribution of the at least one region and coupling the at least one region with indicia of location of the at least one detected object.

Claims

1. A computer implemented method for localizing objects within an image, comprising:

accessing at least one object model representing visual word distributions of at least one training object within training images;

detecting whether an image comprises at least one object based on the at least one object model;

identifying at least one region of the image that corresponds with the at least one detected object and is associated with a minimal dissimilarity between the visual word distribution of the at least one detected object and a visual word distribution of the at least one region; and

coupling the at least one region with indicia of location of the at least one detected object.

2. The method of claim 1, wherein detecting whether the image comprises the at least one object further comprising:

extracting visual words from the image to determine visual word occurrence frequencies;

for each object of the at least one object model, computing a likelihood of being present within the image based on the visual word occurrence frequencies; and

identifying an object having a likelihood that exceeds a predefined threshold.

3. The method of claim 1, wherein the at least one identified region are connected and form a continuous portion of the image.

4. The method of claim 1, wherein identifying the at least one region of the image further comprises for each of the at least one detected object, performing a similarity comparison between a corresponding visual word distribution of the at least one object model and image visual word distributions.

5. The method of claim 4, wherein identifying the at least one region further comprises repeating the performing step for at least one subset of regions within the image.

6. The method of claim 4, wherein performing the similarity comparison further comprises computing a similarity cost between the corresponding visual word distribution of the at least one object model and the visual word distribution of the at least one region.

7. The method of claim 6, wherein the similarity cost comprises a Kullback-Leiber divergence from the corresponding visual word distribution of the at least one object model to the visual word distribution of the at least one region.

8. The method of claim 1 further comprising merging the at least one identified region to form the at least one object.

9. A computer implemented method of localizing objects within an image, comprising:

extracting visual words from an image to determine a visual word distribution;

segmenting the image into a plurality of regions, wherein each of the plurality of regions comprises at least one of the extracted visual words;

minimizing a dissimilarity between at least one object model for defining at least one object and at least one visual word distribution for at least one region of the plurality of regions, wherein the at least one region forms the at least one object;

coupling the at least one region with indicia of location as to the at least one object.

10. The method of claim 9 further comprising merging the at least one region, wherein the at least one region are connected.

11. The method of claim 9, wherein minimizing the dissimilarity further comprises for each of the at least one detected object, performing a similarity comparison between a corresponding visual word distribution of the at least one object model and an image visual word distribution.

12. The method of claim 11, wherein identifying the at least one region further comprises repeating the performing step for at least one subset of regions within the image.

13. An apparatus for localizing objects within an image, comprising:

an examination module for accessing at least one object model representing visual word distributions of at least one training object within training images and detecting whether an image comprises at least one object based on the at least one object model; and

a localization module for identifying at least one region of the image that corresponds with the at least one detected object and is associated with a minimal dissimilarity between the visual word distribution of the at least one detected object and a visual word distribution of the at least one region and coupling the at least one region with indicia of location of the at least one detected object.

14. The apparatus of claim 13, wherein the examination module extracts visual words from the image to determine visual word occurrence frequencies, computes, for each object of the at least one object model, a likelihood of being present within the image based on the visual word occurrence frequencies and identifies an object having a likelihood that exceeds a predefined threshold.

15. The apparatus of claim 13, wherein the at least one identified region comprises at least two connected regions of the image.

16. The apparatus of claim 15, wherein the localization module merges the at least two connected regions to form the at least one object.

17. The apparatus of claim 13, wherein the localization module, for each of the at least one detected object, performs a similarity comparison between a corresponding visual word distribution of the at least one object model and image visual word distributions.

18. The apparatus of claim 17, wherein the localization module repeats the similarity comparison for at least one subset of regions within the image.

19. The apparatus of claim 17, wherein the localization module computes a similarity cost between the corresponding visual word distribution of the at least one object model and the visual word distribution of the at least one region.

20. The apparatus of claim 19, wherein the similarity cost comprises a Kullback-Leiber divergence from the corresponding visual word distribution of the at least one object model to the visual word distribution of the at least one region.