Image classification and labeling
AssigneeSee-Out Pty Ltd
InventorsSandra Mau, Sabesan Sivapalan
Life of this patent
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Abstract
A method of training an image classification model includes obtaining training images associated with labels, where two or more labels of the labels are associated with each of the training images and where each label of the two or more labels corresponds to an image classification class. The method further includes classifying training images into one or more classes using a deep convolutional neural network, and comparing the classification of the training images against labels associated with the training images. The method also includes updating parameters of the deep convolutional neural network based on the comparison of the classification of the training images against the labels associated with the training images.
Claims20
A method of classifying images using one or more image classification models, comprising:
- obtaining, by processing circuitry, training images associated with labels, wherein at least one training image of the training images is associated with two or more labels of the labels, each label corresponding to an image classification class, the labels having a hierarchical structure;
- training, by the processing circuitry, at least two convolutional neural networks using the training images and the hierarchically-structured labels associated with the training images, a separate convolutional neural network being trained for each level of the hierarchical structure;
- and
- classifying, by the processing circuitry, an input image into two or more classes based on the trained at least two convolutional neural networks, the classifying the input image including
- multiplying a probability score output from a first trained convolutional neural network for a label by a probability score output from a second trained convolutional neural network for the label.
An apparatus for classifying images using one or more image classification models, comprising:
- processing circuitry configured to
- obtain training images associated with labels, wherein at least one training image of the training images is associated with two or more labels of the labels, each label corresponding to an image classification class, the labels having a hierarchical structure,
- train at least two convolutional neural networks using the training images and the hierarchically-structured labels associated with the training images, a separate convolutional neural network being trained for each level of the hierarchical structure, and
- classify, by multiplying a probability score output from a first trained convolutional neural network for a label by a probability score output from a second trained convolutional neural network for the label, an input image into two or more classes based on the trained at least two convolutional neural networks.
A non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by a computer, cause the computer to perform a method for classifying images using one or more image classification models, comprising:
- obtaining training images associated with labels, wherein at least one training image of the training images is associated with two or more labels of the labels, each label corresponding to an image classification class, the labels having a hierarchical structure;
- training at least two convolutional neural networks using the training images and the hierarchically-structured labels associated with the training images, a separate convolutional neural network being trained for each level of the hierarchical structure;
- and
- classifying, by multiplying a probability score output from a first trained convolutional neural network for a label by a probability score output from a second trained convolutional neural network for the label, an input image into two or more classes based on the trained at least two convolutional neural networks.
Dependencies are inferred from the claim text.