Image classification system
AssigneeExpedia, Inc.
InventorsLi Wen, Zhanpeng Huo, Jingya Jiang
Life of this patent
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Abstract
An image classification system is provided for determining a likely classification of an image using multiple machine learning models that share a base machine learning model. The image classification system may be a browser-based system on a user computing device that obtains multiple machine learning models over a network from a remote system once, stores the models locally in the image classification system, and uses the models multiple times without needing to subsequently request the machine learning models again from the remote system. The image classification system may therefore determine likely a classification associated with an image by running the machine learning models on a user computing device.
Claims20
A system for classifying images, the system comprising:
- a data store comprising a plurality of machine learning models, the plurality of machine learning models comprising a base model and a plurality of work models;
- and
- one or more processors configured by computer-executable instructions to at least:
- receive an image selected by a user for classification as one of a plurality of item labels;
- generate a base model output vector using the base model and the image, wherein the base model output vector represents a plurality of features of the image;
- determine a first item label associated with the image using the base model output vector and a first work model of the plurality of work models;
- determine a category group label associated with the image using the base model output vector and a second work model of the plurality of work models;
- receive a first user input indicating selection of the category group label;
- determine a category label associated with the image using the base model output vector and a third work model of the plurality of work models, wherein the third work model is associated with the category group label;
- receive a second user input indicating selection of the category label;
- determine a second item label associated with the image using the base model output vector and a fourth work model of the plurality of work models, wherein the fourth work model is associated with the category label;
- receive a third user input indicating selection of the second item label;
- and
- transmit image and classification data associated with the image, wherein the classification data represents the second item label.
A non-transitory machine-readable storage medium encoded with instructions executable by a processor of a computing device, the machine-readable storage medium comprising instructions to:
- receive an image for classification as one of a plurality of item labels;
- generate a base model output using a base model and the image, wherein the base model output represents a plurality of features of the image;
- determine a first item label associated with the image using the base model output and a first work model of a plurality of work models;
- determine a category group label associated with the image using the base model output and a second work model of the plurality of work models;
- determine a category label associated with the image using the base model output and a third work model of the plurality of work models, wherein the third work model is associated with the category group label;
- determine a second item label associated with the image using the base model output and a fourth work model of the plurality of work models, wherein the fourth work model is associated with the category label;
- and
- transmit image and classification data associated with the image, wherein the classification data represents the second item label.
A computer-implemented method comprising:
- receiving an image selected by a user for classification as one of a plurality of item labels;
- determining a first item label associated with the image using a base model output vector and a first work model of a plurality of work models, wherein the base model output vector represents a plurality of features of the image;
- determining a category group label associated with the image using the base model output vector and a second work model of the plurality of work models;
- receiving a first user input indicating selection of the category group label;
- determining a category label associated with the image using the base model output vector and a third work model of the plurality of work models, wherein the third work model is associated with the category group label;
- receiving a second user input indicating selection of the category label;
- determining a second item label associated with the image using the base model output vector and a fourth work model of the plurality of work models, wherein the fourth work model is associated with the category label;
- receiving a third user input indicating selection of the second item label;
- and
- transmitting image and classification data associated with the image, wherein the classification data represents the second item label.
Dependencies are inferred from the claim text.