NATURAL AUGMENTATION OF IMAGE TRAINING DATASETS

1 . A method for augmenting training data for training a machine learning model, the method comprising:

obtaining a first image with geographic location information and associated with a label;

associating with the label a second image including at least a portion having substantially the same geographic location information as the at least one portion of the first image;

forming a training dataset comprising the first and second images as input images and the label that the first and second images are associated with as outputs; and

training the machine learning model using the augmented training dataset.

2 . The method of claim 1 , wherein the geographic location information of the at least one portion of the first image comprises geographic location information of the at least one portion of the first image.

3 . The method of claim 1 , wherein the first and second images are acquired under different conditions.

4 . The method of claim 3 , wherein the different conditions include time, lighting, angle of view, or image resolution.

5 . The method of claim 1 , wherein the geographic location information of the respective portions of the first and second images comprise two-dimensional (or higher) coordinates.

6 . The method of claim 1 , wherein the forming the training dataset comprise selecting or rejecting a candidate image as the second image based on a degree of similarity between the candidate image and the first image.

7 . The method of claim 1 , further comprising:

forming a third image by mirroring or rotating the first image or adding artificial noise to the first or second image; and

associating the third image with the label;

wherein forming the training dataset further comprises including the third image as an input image and the label that the third image is associated with as an output.

8 . The method of claim 1 , wherein the first and second images are substantially mutually spatially registered.

9 . A computer readable medium that stores a set of instructions which when executed perform a method for training a machine learning device, the method comprising:

ascertaining geographic location information of at least one portion of a first image associated with a label;

associating with the label a second image including at least a portion having substantially the same geographic location information as the at least one portion of the first image;

forming a training dataset comprising the first and second images as input images and the label that the first and second images are associated with as outputs; and

training the machine learning device using the dataset.

10 . The computer readable medium of claim 9 , wherein the geographic location information of the respective portions of the first and second images comprise three-dimensional coordinates.

11 . The computer readable medium of claim 10 , wherein the forming the training dataset comprise selecting or rejecting a candidate image as the second image based on a degree of similarity between the candidate image and the first image.

12 . The computer readable medium of claim 9 , wherein the geographic location information of the at least one portion of the first image comprises geographic location information of the at least one portion of the first image.

13 . The computer readable medium of claim 9 , further comprising:

forming a third image by mirroring or rotating the first image or adding artificial noise to the first or second image; and

associating the third image with the label;

wherein forming the training dataset further comprises including the third image as an input image and the label that the third image is associated with as an output.

14 . The computer readable medium of claim 9 , wherein the first and second images are substantially mutually registered.

15 . A system for augmenting a label comprising:

a non-transitory memory storage; and

a processing unit coupled to the non-transitory memory storage, wherein the processing unit is operative to execute a set of instructions read from the non-transitory memory storage to:

ascertain geographic location information of at least one portion of a first image associated with a label;

associate with the label a second image including at least a portion having substantially the same geographic location information as the at least one portion of the first image;

form a training dataset comprising the first and second images as input images and the label that the first and second images are associated with as outputs; and

train a machine learning device using the dataset.

16 . The system of claim 15 , wherein the geographic location information of the respective portions of the first and second images comprise three-dimensional coordinates.

17 . The system of claim 16 , wherein the forming the training dataset comprise selecting or rejecting a candidate image as the second image based on a degree of similarity between the candidate image and the first image.

18 . The system of claim 15 , wherein the geographic location information of the at least one portion of the first image comprises geographic location information of the at least one portion of the first image.

19 . The system of claim 15 , the processing unit is further operative to, upon executing a set of instructions stored on the non-transitory memory storage:

form a third image by mirroring or rotating the first image or adding artificial noise to the first or second image; and

associate the third image with the label;

wherein forming the training dataset further comprises including the third image as an input image and the label that the third image is associated with as an output.

20 . The system of claim 15 , wherein the first and second images are substantially mutually registered.