Image annotation based on label consensus
First Claim
1. A computer-implemented method executed by one or more processors, the method comprising:
- receiving, by the one or more processors, an initial data set comprising a plurality of images, each image from the plurality of images being associated with a set of labels, wherein each label in the set of labels is assigned to the image of the plurality of images by an initial model, the initial model being trained for a particular ground-truth label;
for each image in the plurality of images in the initial data set, predicting, using the initial model, a set of top k predicted labels for the initial data set;
determining, from the sets of top k predicted labels, a set of unique labels;
for each unique label in the set of unique labels;
selecting a respective set of training images for the unique label;
predicting, using the initial model, a set of top k predicted labels for the respective set of training images; and
generating, for each label of the top k predicted labels, a value based on the number of times the respective label occurs in the top k predicted labels in respective set of training images for the unique label;
generating, from the values of each of the labels in the top k predicted labels, a mapping of the unique label to labels in the top k predicted labels that indicates relative strengths of the unique label to the labels in the top k predicted labels;
determining, from the mapping, categories for each unique label; and
determining, for each image in the initial data set, a primary category of the image based on the list of categories of the set of labels for the image.
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Abstract
Implementations include actions of receiving an initial data set including a plurality of images, each image being associated with a set of labels, wherein each label in the set of labels is assigned to a respective image of the plurality of images by an initial model, the initial model being specific to a ground-truth label; for each image in the plurality of images: providing a list of categories associated with a respective image based on a respective set of labels, and determining a primary category of the respective image based on the list of categories; determining a category of the ground-truth label; and providing a revised data set based on the initial data set by comparing the category to primary categories of respective images in the plurality of images, the initial model being trained based on the revised data set to provide a revised model.
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Citations
18 Claims
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1. A computer-implemented method executed by one or more processors, the method comprising:
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receiving, by the one or more processors, an initial data set comprising a plurality of images, each image from the plurality of images being associated with a set of labels, wherein each label in the set of labels is assigned to the image of the plurality of images by an initial model, the initial model being trained for a particular ground-truth label; for each image in the plurality of images in the initial data set, predicting, using the initial model, a set of top k predicted labels for the initial data set; determining, from the sets of top k predicted labels, a set of unique labels; for each unique label in the set of unique labels; selecting a respective set of training images for the unique label; predicting, using the initial model, a set of top k predicted labels for the respective set of training images; and generating, for each label of the top k predicted labels, a value based on the number of times the respective label occurs in the top k predicted labels in respective set of training images for the unique label; generating, from the values of each of the labels in the top k predicted labels, a mapping of the unique label to labels in the top k predicted labels that indicates relative strengths of the unique label to the labels in the top k predicted labels; determining, from the mapping, categories for each unique label; and determining, for each image in the initial data set, a primary category of the image based on the list of categories of the set of labels for the image. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system comprising:
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a data store for storing data; and one or more processors configured to interact with the data store, the one or more processors being further configured to perform operations comprising; receiving, by the one or more processors, an initial data set comprising a plurality of images, each image from the plurality of images being associated with a set of labels, wherein each label in the set of labels is assigned to the image of the plurality of images by an initial model, the initial model being trained for a particular ground-truth label; for each image in the plurality of images in the initial data set, predicting, using the initial model, a set of top k predicted labels for the initial data set; determining, from the sets of top k predicted labels, a set of unique labels; for each unique label in the set of unique labels; selecting a respective set of training images for the unique label; predicting, using the initial model, a set of top k predicted labels for the respective set of training images; and generating, for each label of the top k predicted labels, a value based on the number of times the respective label occurs in the top k predicted labels in respective set of training images for the unique label; generating, from the values of each of the labels in the top k predicted labels, a mapping of the unique label to labels in the top k predicted labels determining, from the mapping, categories for each unique label; and determining, for each image in the initial data set, a primary category of the image based on the list of categories of the set of labels for the image. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
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receiving, by the one or more processors, an initial data set comprising a plurality of images, each image from the plurality of images being associated with a set of labels, wherein each label in the set of labels is assigned to the image of the plurality of images by an initial model, the initial model being trained for a particular ground-truth label; for each image in the plurality of images in the initial data set, predicting, using the initial model, a set of top k predicted labels for the initial data set; determining, from the sets of top k predicted labels, a set of unique labels; for each unique label in the set of unique labels; selecting a respective set of training images for the unique label; predicting, using the initial model, a set of top k predicted labels for the respective set of training images; and generating, for each label of the top k predicted labels, a value based on the number of times the respective label occurs in the top k predicted labels in respective set of training images for the unique label; generating, from the values of each of the labels in the top k predicted labels, a mapping of the unique label to labels in the top k predicted labels determining, from the mapping, categories for each unique label; and determining, for each image in the initial data set, a primary category of the image based on the list of categories of the set of labels for the image. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification