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US 10,474,949 B2Granted
Knowledge-graph biased classification for data
AssigneeQUALCOMM Incorporated
InventorsSomdeb Majumdar, Regan Blythe Towal, Sachin Subhash Talathi, David Jonathan Julian, Venkata Sreekanta Reddy Annapureddy
Life of this patent
Estimating the term…
Abstract
A method for classifying an object includes applying multiple confidence values to multiple objects. The method also includes determining a metric based on the multiple confidence values. The method further includes determining a classification of a first object from the multiple objects based on a knowledge-graph when the metric is above a threshold.
Claims15
A method for computer-implemented tagging of objects in an image, the method comprising:
- processing the image by a computer-implemented artificial neural network to identify at least a first and a second object and generate, for at least the first object, at least a first and a second predicted classification and corresponding confidence values, the first object being partially visible in the image;
- generating an adjusted first confidence value by adjusting the confidence value corresponding to the first predicted classification based on a predetermined co-existence probability for the first predicted classification and the second object and also at least one of a physical location of an image capturing device or a time when the image was captured;
- generating an adjusted second confidence value by adjusting the confidence value corresponding to the second predicted classification based on a predetermined co-existence probability for the second predicted classification and the second object and also at least one of the physical location of the image capturing device or the time when the image was captured;
- determining a classification for the first object based on the first and second predicted classifications and the corresponding adjusted first and second confidence values;
- and
- generating a tag for the first object using the determined classification.
3 more elements
A computer apparatus comprising a memory and a processor coupled to the memory, wherein the processor is adapted to:
- process an image by a computer-implemented artificial neural network to identify at least a first and a second object and generate, for at least the first object, at least a first and a second predicted classification and corresponding confidence values, the first object being partially visible in the image;
- generate an adjusted first confidence value by adjusting the confidence value corresponding to the first predicted classification based on a predetermined co-existence probability for the first predicted classification and the second object and also at least one of a physical location of an image capturing device or a time when the image was captured;
- generate an adjusted second confidence value by adjusting the confidence value corresponding to the second predicted classification based on a predetermined co-existence probability for the second predicted classification and the second object and also at least one of the physical location of the image capturing device or the time when the image was captured;
- determine a classification for the first object based on the first and second predicted classifications and the corresponding adjusted first and second confidence values;
- and
- generate a tag for the first object using the determined classification.
3 more elements
A non-transitory computer-readable medium having program code recorded thereon for computer-implemented tagging of objects in an image, the program code being executed by a processor and comprising:
- program code to process the image by a computer-implemented artificial neural network to identify at least a first and a second object and generate, for at least the first object, at least a first and a second predicted classification and corresponding confidence values, the first object being partially visible in the image;
- program code to generate an adjusted first confidence value by adjusting the confidence value corresponding to the first predicted classification based on a predetermined co-existence probability for the first predicted classification and the second object and also at least one of a physical location of an image capturing device or a time when the image was captured;
- program code to generate an adjusted second confidence value by adjusting the confidence value corresponding to the second predicted classification based on a predetermined co-existence probability for the second predicted classification and the second object and also at least one of the physical location of the image capturing device or the time when the image was captured;
- program code to determine a classification for the first object based on the first and second predicted classifications and the corresponding adjusted first and second confidence values;
- and
- program code to generate a tag for the first object using the determined classification.
3 more elements
Dependencies are inferred from the claim text.