Methods for object localization and image classification
AssigneeQUALCOMM Incorporated
InventorsDaniel Hendricus Franciscus Dijkman, David Jonathan Julian
Life of this patent
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Abstract
A method of training for image classification includes labelling a crop from an image including an object of interest. The crop may be labelled with an indication of whether the object of interest is framed, partially framed or not present in the crop. The method may also include assigning a fully framed class to the labelled crop, including the object of interest, if the object of interest is framed. A labelled crop may be assigned a partially framed class if the object of interest is partially framed. A background class may be assigned to a labelled crop if the object of interest is not present in the crop.
Claims16
A method for image classification, comprising:
- labelling, by a first artificial neural network, a crop from an image including an object of interest with an indication of whether the object of interest is framed, partially framed, or not present in the crop;
- assigning a fully framed class to the labelled crop, including the object of interest, when the object of interest is framed;
- assigning a partially framed class to the labelled crop, including the object of interest, when the object of interest is partially framed;
- assigning a background class to the labelled crop when the object of interest is not present in the crop;
- and
- classifying the object of interest in the image based on a class assigned to the labelled crop.
A method of training an artificial neural network for image classification, comprising:
- selecting a crop from an image;
- determining whether the crop of the image includes an area surrounded by a predetermined bounding box;
- discarding, prior to the training, the crop when the crop of the image does not include a portion of the area surrounded by the predetermined bounding box;
- and
- training the artificial neural network to determine a label of the crop of the image including an object of interest with an indication of whether the object of interest is framed, partially framed, or not present in the crop, the artificial network trained for the image classification of the object of interest in the image with a plurality of crops from the image, the classification based on a class assigned to the labelled crop, each crop comprising at least a portion of the area surrounded by the predetermined bounding box.
An apparatus for image classification, comprising:
- a memory;
- and
- at least one processor coupled to the memory, the at least one processor configured:
- to label, by a first artificial neural network, a crop from an image, including an object of interest with an indication of whether the object of interest is framed, partially framed, or not present in the crop;
- to assign a fully framed class to the labelled crop, including the object of interest, when the object of interest is framed;
- to assign a partially framed class to the labelled crop, including the object of interest, when the object of interest is partially framed;
- to assign a background class to the labelled crop when the object of interest is not present in the crop;
- and
- to classify the object of interest in the image based on a class assigned to the labelled crop.
An apparatus for training an artificial neural network for image classification, comprising:
- a memory;
- and
- at least one processor coupled to the memory, the at least one processor configured:
- to select a crop from an image;
- to determine whether the crop of the image includes an area surrounded by a predetermined bounding box;
- to discard, prior to the training, the crop when the crop of the image does not include a portion of the area surrounded by the predetermined bounding box;
- and
- to train the artificial neural network to determine a label of the crop of the image including an object of interest with an indication of whether the object of interest is framed, partially framed, or not present in the crop, the artificial network trained for the image classification of the object of interest in the image with a plurality of crops from the image, the classification based on a class assigned to the labelled crop, each crop comprising at least a portion of the area surrounded by the predetermined bounding box.
Dependencies are inferred from the claim text.