Zero shot machine vision system via joint sparse representations
AssigneeHRL Laboratories, LLC
InventorsSoheil Kolouri, Shankar R. Rao, Kyungnam Kim
Life of this patent
Estimating the term…
Abstract
Described is a system that can recognize novel objects that the system has never before seen. The system uses a training image set to learn a model that maps visual features from known images to semantic attributes. The learned model is used to map visual features of an unseen input image to semantic attributes. The unseen input image is classified as belonging to an image class with a class label. A device is controlled based on the class label.
Claims20
A system for object recognition, the system comprising:
- one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of:
- using a training image set, learning a model that maps visual features from known images to semantic attributes;
- using the learned model to map visual features of an unseen input image to semantic attributes;
- classifying, with a class label, the unseen input image as belonging to an image class;
- and
- controlling a device based on the class label.
A computer implemented method for object recognition, the method comprising an act of:
- causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
- using a training image set, learning a model that maps visual features from known images to semantic attributes;
- using the learned model to map visual features of an unseen input image to semantic attributes;
- classifying, with a class label, the unseen input image as belonging to an image class;
- and
- controlling a device based on the class label.
A computer program product for object recognition, the computer program product comprising:
- computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:
- using a training image set, learning a model that maps visual features from known images to semantic attributes;
- using the learned model to map visual features of an unseen input image to semantic attributes;
- classifying, with a class label, the unseen input image as belonging to an image class;
- and
- controlling a device based on the class label.
Dependencies are inferred from the claim text.