| CPC G06N 3/08 (2013.01) [G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06N 3/047 (2023.01)] | 21 Claims |
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1. A machine-trained method for analyzing an input image to determine whether the input image contains a picture of an object belonging to one object category from a plurality of object categories, the method comprising:
propagating an input image through a plurality of layers of a machine-trained (MT) network to produce a multi-dimensional codeword representing the input image in a multi-dimensional space that includes a first set of codewords comprising at least one codeword for each object category in the plurality of object categories, wherein each codeword in the first set of codewords represents one of the object categories, wherein propagating the input image through the plurality of layers comprises providing the input image to a first input layer of processing nodes that generate a first set of intermediate outputs that are subsequently provided to at least one subsequent layer of the MT network, each subsequent layer of the MT network comprising a respective set of processing nodes that receive intermediate outputs from at least one previous layer of the MT network and generate a respective set of intermediate outputs until an output layer of the MT network generates the multi-dimensional codeword representing the input image;
for the produced codeword representing the input image, generating a set of affinity scores with each affinity score identifying a proximity of the produced codeword to a different codeword in a second set of previously defined codewords representing other images, wherein each codeword in the second set of codewords is located at least a particular distance in the multi-dimensional space from any of the codewords in the first set of codewords in order to define boundaries between the codewords in the first set of codewords representing the object categories in the multi-dimensional space;
comparing the set of affinity scores generated for the produced codeword representing the input image with sets of affinity scores previously generated for the first-set codewords, wherein each set of affinity scores that is previously generated for each particular first-set codeword comprises a plurality of affinity scores each of which identifies a proximity of the particular first-set codeword representing an object category to a different codeword in the second set of codewords defining boundaries between the object categories; and
based on said comparison, identifying an object category to associate with the input image, said association specifying that the input image contains a picture of an object that belongs to the identified object category.
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