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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for classifying an object, the method comprising: applying a plurality of confidence values to a plurality of objects; determining a metric based at least in part on the plurality of confidence values; and determining a classification of a first object of the plurality of objects based at least in part on a knowledge-graph when the metric is above a threshold. 2. The method of claim 1, in which the knowledge-graph includes weights determined based at least in part on a likelihood of the first object being associated with at least a second object. 3. The method of claim 2, in which the classification is determined based at least in part on a biasing of the plurality of confidence values for the first object with the weights of the knowledge-graph. 4. The method of claim 2, further comprising associating the weights of the knowledge-graph with an object of the plurality of objects having a greatest confidence value. 5. The method of claim 2, in which the weights of the knowledge-graph are based at least in part on learned relationships of objects. 6. The method of claim 5, further comprising updating the weights of the knowledge-graph based at least in part on learning relationships of objects from the plurality of objects having a confusion value that is less than the threshold. 7. The method of claim 5, further comprising updating the weights of the knowledge-graph based at least in part on user input. 8. The method of claim 2, in which the weights of the knowledge-graph are based at least in part on an order of when the first object appears relative to the second object. 9. The method of claim 1, in which the knowledge-graph is integrated with a deep convolutional network (DCN). 10. The method of claim 1, in which the metric is a confusion metric based at least in part on confidence values for each predicted class. 11. The method of claim 1, in which the plurality of objects are captured in at least an image, video, audio, or a combination thereof. 12. An apparatus for classifying an object, the apparatus comprising: a memory unit; and at least one processor coupled to the memory unit, the at least one processor being configured: to apply a plurality of confidence values to a plurality of objects; to determine a metric based at least in part on the plurality of confidence values; and to determine a classification of a first object of the plurality of objects based at least in part on a knowledge-graph when the metric is above a threshold. 13. The apparatus of claim 12, in which the knowledge-graph includes weights determined based at least in part on a likelihood of the first object being associated with at least a second object. 14. The apparatus of claim 13, in which the at least one processor is further configured to determine the classification based at least in part on a biasing of the plurality of confidence values for the first object with the weights of the knowledge-graph. 15. The apparatus of claim 13, in which the at least one processor is further configured to associate the weights of the knowledge-graph with an object of the plurality of objects having a greatest confidence value. 16. The apparatus of claim 13, in which the weights of the knowledge-graph are based at least in part on learned relationships of objects. 17. The apparatus of claim 16, in which the at least one processor is further configured to update the weights of the knowledge-graph based at least in part on learning relationships of objects from the plurality of objects having a confusion value that is less than the threshold. 18. The apparatus of claim 16, in which the at least one processor is further configured to update the weights of the knowledge-graph based at least in part on user input. 19. The apparatus of claim 13, in which the weights of the knowledge-graph are based at least in part on an order of when the first object appears relative to the second object. 20. The apparatus of claim 12, in which the knowledge-graph is integrated with a deep convolutional network (DCN). 21. The apparatus of claim 12, in which the metric is a confusion metric based at least in part on confidences for each predicted class. 22. The apparatus of claim 12, in which the plurality of objects are captured in at least an image, video, audio, or a combination thereof. 23. A computer program product for classifying an object, the computer program product comprising: a non-transitory computer-readable medium having program code recorded thereon, the program code being executed by a processor and comprising: program code to apply a plurality of confidence values to a plurality of objects; program code to determine a metric based at least in part on the plurality of confidence values; and program code to determine a classification of a first object of the plurality of objects based at least in part on a knowledge-graph when the metric is above a threshold. 24. An apparatus for classifying an object, the apparatus comprising: means for applying a plurality of confidence values to a plurality of objects; means for determining a metric based at least in part on the plurality of confidence values; and means for determining a classification of a first object of the plurality of objects based at least in part on a knowledge-graph when the metric is above a threshold.