METHODS FOR OBJECT LOCALIZATION AND IMAGE CLASSIFICATION

• US 20170169314A1
• Filed: 08/25/2016
• Published: 06/15/2017
• Est. Priority Date: 12/15/2015
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: DIJKMAN, Daniel Hendricus Franciscus, JULIAN, David Jonathan
• Granted Patent: US 10,318,848 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06F 18/2148 characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06F 18/2163 Partitioning the feature space
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06F 18/2431 Multiple classes
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 10/771 Feature selection, e.g. selecting representative features from a multi-dimensional feature space

Abstract

A method of training for image classification includes labelling a crop from an image including an object of interest. The crop may be labelled with an indication of whether the object of interest is framed, partially framed or not present in the crop. The method may also include assigning a fully framed class to the labelled crop, including the object of interest, if the object of interest is framed. A labelled crop may be assigned a partially framed class if the object of interest is partially framed. A background class may be assigned to a labelled crop if the object of interest is not present in the crop.

Claims

1. A method of training for image classification, comprising:

labelling a crop from an image including an object of interest with an indication of whether the object of interest is framed, partially framed or not present in the crop;

assigning a fully framed class to the labelled crop, including the object of interest, if the object of interest is framed;

assigning a partially framed class to the labelled crop, including the object of interest, if the object of interest is partially framed; and

assigning a background class to the labelled crop if the object of interest is not present in the crop.

2. The method of claim 1, further comprising weighting a class estimate from another network based on whether the labelled crop is indicated as background.

3. The method of claim 1, further comprising:

determining a proportion of the object of interest that is included in the crop; and

comparing the determined proportion to at least one threshold value, and in which the labelling is based on the comparing.

4. A method of training for image classification, comprising:

selecting a crop from an image;

determining whether the crop of the image includes an area surrounded by a predetermined bounding box; and

discarding the crop if the crop of the image does not include a portion of the area surrounded by the predetermined bounding box.

5. The method of claim 4, further comprising generating a proposed bounding box for the image if the image lacks the predetermined bounding box, before discarding the crop if the crop of the image does not include the portion.

6. The method of claim 5, further comprising:

selecting a new crop from the image;

determining whether the new crop of the image includes the area surrounded by the proposed bounding box; and

discarding the new crop if the new crop of the image does not include a portion of the area surrounded by the proposed bounding box.

7. The method of claim 5, in which the generating comprises:

localizing at least one object in the image; and

adding a border around the localized at least one object.

8. The method of claim 7, further comprising:

selecting a second crop including the proposed bounding box;

determining a proportion of the localized at least one object that is included in the second crop; and

updating the proposed bounding box based on the determined proportion.

9. An apparatus for training for image classification, comprising:

a memory; and

at least one processor coupled to the memory, the at least one processor configured;

to label a crop from an image including an object of interest with an indication of whether the object of interest is framed, partially framed or not present in the crop;

to assign a fully framed class to the labelled crop, including the object of interest, if the object of interest is framed;

to assign a partially framed class to the labelled crop, including the object of interest, if the object of interest is partially framed; and

to assign a background class to the labelled crop if the object of interest is not present in the crop.

10. The apparatus of claim 9, in which the at least one processor is further configured to weight a class estimate from another network based on whether the labelled crop is indicated as background.

11. The apparatus of claim 9, in which the at least one processor is further configured:

to determine a proportion of the object of interest that is included in the crop; and

to compare the determined proportion to at least one threshold value, and in which the labelling is based on the comparing.

12. An apparatus for training for image classification, comprising:

a memory; and

at least one processor coupled to the memory, the at least one processor configured;

to select a crop from an image;

to determine whether the crop of the image includes an area surrounded by a predetermined bounding box; and

to discard the crop if the crop of the image does not include a portion of the area surrounded by the predetermined bounding box.

13. The apparatus of claim 12, in which the at least one processor is further configured to generate a proposed bounding box for the image if the image lacks the predetermined bounding box, before discarding the crop if the crop of the image does not include the portion.

14. The apparatus of claim 13, in which the at least one processor is further configured:

to select a new crop from the image;

to determine whether the new crop of the image includes the area surrounded by the proposed bounding box; and

to discard the new crop if the new crop of the image does not include a portion of the area surrounded by the proposed bounding box.

15. The apparatus of claim 13 in which in which the at least one processor is further configured to generate the proposed bounding box:

by localizing at least one object in the image; and

by adding a border around the localized at least one object.

16. The apparatus of claim 15, in which the at least one processor is further configured:

to select a second crop including the proposed bounding box;

to determine a proportion of the localized at least one object that is included in the second crop; and

to update the proposed bounding box based on the determined proportion.