DEVICES, SYSTEMS, AND METHODS FOR LARGE-SCALE LINEAR DISCRIMINANT ANALYSIS OF IMAGES

• US 20150078655A1
• Filed: 09/18/2013
• Published: 03/19/2015
• Est. Priority Date: 09/18/2013
• Status: Abandoned Application

• Current Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Original Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Inventors: Lu, Juwei, Denney, Bradley Scott, Huang, Hung Khei
• Application Number:
  US14/030,861
• Publication Number:
  US 20150078655A1
• Time in Patent Office: Days
• US Class Current: 382/159
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting

Abstract

Systems, devices, and methods for generating hierarchical subspace maps obtain a training set of images, wherein the images in the training set of images are each associated with at least one category in a plurality of categories; organize the images in the training set of images into a category hierarchy based on the training set of images and on the plurality of categories, wherein the category hierarchy identifies each of the categories in the plurality of categories as at least one of a parent category and child category; and generate a subspace map for each parent category based on images associated with respective child categories of the parent category, thereby generating a plurality of subspace maps.

Claims

1. A method comprising:

obtaining a training set of images, wherein the images in the training set of images are each associated with at least one category in a plurality of categories;

organizing the images in the training set of images into a category hierarchy based on the training set of images and on the plurality of categories, wherein the category hierarchy identifies each of the categories in the plurality of categories as at least one of a parent category and child category; and

generating a subspace map for each parent category based on images associated with respective child categories of the parent category, thereby generating a plurality of subspace maps.

2. The method of claim 1, wherein the subspace maps are LDA subspace maps.

3. The method of claim 2, wherein the subspace maps are regularized LDA subspace maps.

4. The method of claim 1, wherein generating the category hierarchy is further based on semantic distances between the categories.

5. The method of claim 1, wherein some categories in the category hierarchy are both child categories and parent categories.

6. The method of claim 1, wherein generating the subspace map for a parent category includes calculating one or more most-significant eigenvectors in a space defined by representations of image features of the images that are associated with child categories of the parent category.

7. The method of claim 4, wherein generating the category hierarchy is further based on a threshold, and wherein a group of categories is divided into at least two parent categories and two groups of child categories when a number of categories in the group of categories exceeds the threshold.

8. The method of claim 1, further comprising weighting each subspace map.

9. The method of claim 8, wherein the weighting of each subspace map is based on a number of images associated with the respective category that corresponds to the subspace map.

10. The method of claim 8, wherein the weighting of each subspace map is based at least in part on the number of child categories of the parent category.

11. The method of claim 1, further comprising projecting a query image representation with each of the subspace maps, thereby producing a plurality of projections of the query image representation.

12. A computing device comprising:

one or more computer-readable media; and

one or more processors coupled to the computer-readable media and configured to cause the computing device to perform operations including

obtaining a training set of images;

assigning the images to a category in a category hierarchy, wherein the category hierarchy identifies each of the categories in the plurality of categories as at least one of a parent category and child category; and

generating a subspace map for each parent category based on images assigned to respective child categories of the parent category, thereby generating a plurality of subspace maps.

13. The computing device of claim 12, wherein the one or more processors are further configured to cause the computing device to assign a respective weight to each subspace map.

14. The computing device of claim 12, wherein the one or more processor are further configured to cause the computing device to project an input image representation with each of the subspace maps, thereby generating a plurality of subspace projections.

15. The computing device of claim 14, wherein the one or more processor are further configured to cause the computing device to generate a representation of the input image based on the plurality of subspace projections.

16. One or more computer-readable media storing instructions that, when executed by one or more computing devices, cause the computer devices to perform operations comprising:

obtaining a training set of images;

assigning the images to a category in a category hierarchy, wherein the category hierarchy identifies each of the categories in the plurality of categories as at least one of a parent category and child category; and

generating a subspace map for each parent category based on images assigned to respective child categories of the parent category, thereby generating a plurality of subspace maps.

17. The one or more computer-readable media of claim 16, wherein generating the subspace map for each parent category is based on a scatter matrix that is defined by image representations of the images that are associated with the respective child categories of the parent category.

18. The one or more computer-readable media of claim 17, wherein generating the subspace map for each parent category includes calculating eigenvectors based on the scatter matrices.