Image classification
First Claim
1. A computer-implemented method, comprising:
- obtaining a plurality of n-grams, each of the n-grams including a unique set of one or more terms;
for each of the n-grams;
identifying, in a processing device, a plurality of training images for training an image classification model, the plurality of training images comprising;
positive training images having relevance measures, for the n-gram, that satisfy a relevance threshold; and
negative training images having relevance measures, for the n-gram, that do not satisfy the relevance threshold;
selecting, in the processing device, a training image from the plurality of training images, wherein the selecting comprises semi-randomly selecting the training image subject to a selection requirement specifying that a second image be selected with a specified likelihood;
classifying, in the processing device, the training image with the image classification model based on a feature vector of the training image, the feature vector comprising image feature values for the training image; and
training, in the processing device, the image classification model based on the feature vector of the training image and the classification of the training image.
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Accused Products
Abstract
An image classification system trains an image classification model to classify images relative to text appearing with the images. Training images are iteratively selected and classified by the image classification model according to feature vectors of the training images. An independent model is trained for unique n-grams of text. The image classification system obtains text appearing with an image and parses the text into candidate labels for the image. The image classification system determines whether an image classification model has been trained for the candidate labels. When an image classification model corresponding to a candidate label has been trained, the image classification subsystem classifies the image relative to the candidate label. The image is labeled based on candidate labels for which the image is classified as a positive image.
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Citations
26 Claims
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1. A computer-implemented method, comprising:
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obtaining a plurality of n-grams, each of the n-grams including a unique set of one or more terms; for each of the n-grams; identifying, in a processing device, a plurality of training images for training an image classification model, the plurality of training images comprising; positive training images having relevance measures, for the n-gram, that satisfy a relevance threshold; and negative training images having relevance measures, for the n-gram, that do not satisfy the relevance threshold; selecting, in the processing device, a training image from the plurality of training images, wherein the selecting comprises semi-randomly selecting the training image subject to a selection requirement specifying that a second image be selected with a specified likelihood; classifying, in the processing device, the training image with the image classification model based on a feature vector of the training image, the feature vector comprising image feature values for the training image; and training, in the processing device, the image classification model based on the feature vector of the training image and the classification of the training image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A system, comprising:
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a data store storing training images for training an image classification model for an n-gram, the training images including positive images having relevance measures, for the n-gram, that meet a relevance threshold and negative images having relevance measures, for the n-gram, that do not meet the relevance threshold; and an image classification system coupled to the data store, the image classification system including one or more processors configured to train the image classification model for the n-gram based on feature vectors of the training images, the image classification model comprising scalars corresponding to the feature vectors and a minimum kernel of feature vectors of training images. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A non-transitory computer readable medium encoded with a computer program comprising instructions that when executed operate to cause a computer to perform operations:
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obtaining a plurality of n-grams, each of the n-grams including a unique set of one or more terms; for each of the n-grams; identifying a plurality of training images for training an image classification model, the plurality of training images comprising; positive training images having relevance measures, for the n-gram, that satisfy a relevance threshold; and negative training images having relevance measures, for the n-gram, that do not satisfy the relevance threshold; selecting a training image from the plurality of training images, wherein the selecting comprises semi-randomly selecting the training image subject to a selection requirement specifying that a second image be selected with a specified likelihood; classifying the training image with the image classification model based on a feature vector of the training image, the feature vector comprising image feature values for the training image; and training the image classification model based on the feature vector of the training image and the classification of the training image. - View Dependent Claims (21, 22, 23, 24, 25, 26)
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Specification