US 12,182,721 B2 · Deep learning-based anomaly detection in images

Abstract

A method comprising: receiving, as input, training images, wherein at least a majority of the training images represent normal data instances; receiving, as input, a target image; extracting (i) a set of feature representations from a plurality of image locations within each of the training images, and (ii) target feature representations from a plurality of target image locations within the target image; calculating, with respect to a target image location of the plurality of target image locations in the target image, a distance between (iii) the target feature representation of the target image location, and (iv) a subset from the set of feature representations comprising the k nearest the feature representations to the target feature representation; and determining that the target image location is anomalous, when the calculated distance exceeds a predetermined threshold.

Claims20

Dependent claims

A system comprising:

at least one hardware processor;
and
a non-transitory computer-readable storage medium having stored thereon program instruction, the program instructions executable by the at least one hardware processor to:
receive, as input, training images, wherein at least a majority of said training images represent normal data instances,
receive, as input, a target image,
extract (i) a set of feature representations from a plurality of sub-image locations within each of said training images, and (ii) target feature representations from a plurality of target sub-image locations within said target image,
calculate, with respect to a target sub-image location of said plurality of target sub-image locations in said target image, a distance between (iii) said target feature representation of said target sub-image location, and (iv) a subset from said set of feature representations comprising the k nearest said feature representations to said target feature representation, and
determine that said target sub-image location is anomalous, when said calculated distance exceeds a predetermined threshold.

5 more elements

A computer program product comprising a non-transitory computer-readable storage medium having program instructions embodied therewith, the program instructions executable by at least one hardware processor to comprising:

receive, as input, training images, wherein at least a majority of said training images represent normal data instances;
receive, as input, a target image;
extract (i) a set of feature representations from a plurality of sub-image locations within each of said training images, and (ii) target feature representations from a plurality of target sub-image locations within said target image;
calculate, with respect to a target sub-image location of said plurality of target sub-image locations in said target image, a distance between (iii) said target feature representation of said target sub-image location, and (iv) a subset from said set of feature representations comprising the k nearest said feature representations to said target feature representation;
and
determine that said target sub-image location is anomalous, when said calculated distance exceeds a predetermined threshold.

3 more elements

Dependencies are inferred from the claim text.

Deep learning-based anomaly detection in images

Life of this patent

Deep learning-based anomaly detection in images

Life of this patent

Abstract

Claims20