Generating and utilizing normalized scores for classifying digital objects

Patent metadata

Field	Value
Publication number	US-2017193337-A1
Application number	US-201514986585-A
Country	US
Kind code	A1
Filing date	Dec 31, 2015
Priority date	Dec 31, 2015
Publication date	Jul 6, 2017
Grant date	—

Abstract

The present disclosure is directed toward systems and methods that enable more accurate digital object classification. In particular, disclosed systems and methods address inaccuracies in digital object classification introduced by variations in classification scores. Specifically, in one or more embodiments, disclosed systems and methods generate probability functions utilizing digital test objects and transform classifications scores into normalized classification scores utilizing probability functions. Disclosed systems and methods utilize normalized classification scores to more accurately classify and identify digital objects in a variety of applications.

First claim

What is claimed is:

1 . A method comprising:
identifying a set of one or more digital training images tagged with information identifying a known object, the known object portrayed in each image in the set of one or more tagged digital training images; generating, by at least one processor and utilizing the information identifying the known object, a classification score with regard to an unknown object portrayed in a probe digital image, the classification score indicating a likelihood that the unknown object portrayed in the probe digital image corresponds to the known object portrayed in the set of one or more tagged digital training images; transforming the classification score into a normalized classification score based on the number of tagged digital training images in the set of one or more tagged digital training images; and based on the normalized classification score, determining whether the unknown object portrayed in the probe digital image corresponds to the known object portrayed in the set of one or more tagged digital training images.

2 . The method of claim 1 , wherein the normalized classification score comprises a probability that the known object portrayed in the set of one or more digital training images is the unknown object portrayed in the probe digital image.

3 . The method of claim 1 , wherein transforming the classification score into a normalized classification score based on the number of tagged digital training images further comprises: calculating a probability that, assuming the unknown object portrayed in the probe digital image corresponds to the known object portrayed in the set of one or more tagged digital training images, the generated classification score would result based on the number of tagged digital training images.

4 . The method of claim 1 , wherein transforming the classification score into a normalized classification score based on the number of tagged digital training images further comprises:
generating a probability function from a repository of digital test images, the probability function reflecting the probability of generating classification scores given the number of tagged digital training images; and transforming the classification score into the normalized classification score based on the probability function.

5 . The method of claim 4 , further comprising:
identifying a second set of one or more digital training images tagged with information identifying a second known object, the second known object portrayed in each image in the second set of one or more tagged digital training images; generating, by the at least one processor, a second classification score with regard to the unknown object portrayed in the probe digital image, the second classification score indicating a likelihood that the unknown object portrayed in the probe digital image corresponds to the second known object portrayed in the second set of one or more tagged digital training images; transforming the second classification score into a second normalized classification score based on the number of tagged digital training images in the second set of one or more tagged digital training images; and based on the normalized classification score and the second normalized classification score, determining whether the unknown object portrayed in the probe digital image corresponds to the known object portrayed in the set of one or more tagged digital training images.

6 . The method of claim 5 , wherein determining whether the unknown object portrayed in the probe digital image corresponds to the known object portrayed in the set of one or more tagged digital training images comprises: comparing the normalized classification score transformed based on the number of tagged digital training images in the set of one or more tagged digital training images with the second normalized classification score transformed based on the number of tagged digital training images in the second set of one or more tagged digital training images.

7 . The method of claim 6 , wherein
transforming the classification score into the normalized classification score based on the number of tagged digital training images in the set of one or more tagged digital training images further comprises: generating an imposter probability function, the imposter probability function reflecting the probability of returning classification scores based on the number of tagged digital training images in the set of one or more tagged digital training images assuming that the unknown object portrayed in the probe digital image does not correspond to the known object portrayed in the set of one or more tagged digital training images; and transforming the second classification score into the second normalized classification score based on the number of tagged digital training images in the second set of one or more tagged digital training images further comprises: generating a second imposter probability function, the second imposter probability function reflecting the probability of returning classification scores based on the number of tagged digital training images in the second set of one or more tagged digital training images assuming that the unknown object portrayed in the probe digital image does not correspond to the known object portrayed in the second set of one or more tagged digital training images.

8 . The method of claim 7 ,
wherein transforming the classification score into a normalized classification score further comprises:
calculating a probability of generating the classification score based on the number of tagged digital training images in the set of one or more tagged digital training images utilizing the probability function; and
calculating a probability of generating the classification score based on the number of tagged digital training images in the set of one or more tagged digital training images utilizing the imposter probability function; and
wherein transforming the second classification score into a second normalized classification score further comprises:
calculating a probability of generating the second classification score based on the number of tagged digital training images in the second set of one or more tagged digital training images utilizing the second probability function; and
calculating a probability of generating the second classification score based on the number of tagged digital training images in the second set of one or more tagged digital training images utilizing the second imposter probability function.

9 . The method of claim 4 , wherein generating the probability function from the repository of digital test images comprises:
training a plurality of test training models from the repository of digital test images, each test training model being trained with a number of digital test images corresponding to the number of tagged digital training images; generating a plurality of test classification scores utilizing the plurality of test training models; generating a histogram of the plurality of test classification scores; and generating the probability function based on the generated histogram.

10 . A system comprising:
at least one processor; and at least one non-transitory computer readable storage medium storing instructions that, when executed by the at least one processor, cause the system to: identify a set of one or more digital training images tagged with information identifying a known object, the known object portrayed in each image in the set of one or more tagged digital training images; generate, utilizing the information identifying the known object, a classification score with regard to an unknown object portrayed in a probe d

Assignees

• Dropbox Inc

Inventors

• Kriegman David J
• Belhumeur Peter N
• Berg Thomas
• Welinder Nils Peter

Classifications

• G06F18/254 Primary

  of classification results, e.g. of results related to same input data · CPC title

• G06F18/2415

  based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate · CPC title

• G06K9/6277 Primary

  Physics · mapped topic

• G06K9/66

  Physics · mapped topic

• G06K9/6212

  Physics · mapped topic