A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising the following computer-executable acts: causing a graphical user interface to be displayed on a display screen of a computing device, the graphical user interface configured to receive assignations of labels to respective images; receiving, by way of the graphical user interface, an assignment of a first label to a first image in an image collection, the first label identifying an event captured in the digital image; and assigning a second label to a second image in the image collection based upon the first label assigned to the first image, the second label identifying a person captured in the second image. 2. The method of claim 1, further comprising: subsequent to assigning the second label to the second image, requesting feedback as to the second label being assigned to the second image. 3. The method of claim 2, the feedback being confirmation that the second label properly identifies the person captured in the second image. 4. The method of claim 1, further comprising: assigning a third label to a third image in the image collection based upon the first label assigned to the first image and the second label assigned to the second image, the third label identifying a location at which the third image was captured. 5. The method of claim 1, wherein the assigning the second label to the second image comprises computing a probability that the person is captured in the image, the probability based upon a learned relationship between the event captured in the first image and the person captured in the second image. 6. The method of claim 1, further comprising: receiving, by way of the graphical user interface, an assignment of a third label to the first image in the image collection, the third label identifying a location at which the first image was captured; and assigning the second label to the second image based upon the first label assigned to the first image. 7. The method of claim 1, the first label received by a first relational model, the first relational model configured to model relationships between events captured in images of the image collection and people captured in images of the image collection, the first relational model configured to output data that is indicative of a probability that the second image captures the person, and wherein the assigning of the second label is based upon the data that is indicative of the probability. 8. The method of claim 7, further comprising: outputting, by way of the graphical user interface, a plurality of guesses as to labels to assign to respective images of the image collection, the labels corresponding to the guesses being events possibly captured in the respective images; receiving feedback that confirms at least one guess in the plurality of guesses; and assigning the second label to the second image based upon the plurality of guesses and the feedback. 9. The method of claim 8, further comprising updating the first relational model based upon the plurality of guesses and the feedback. 10. The method of claim 7, further comprising: outputting, by way of the graphical user interface, a plurality of guesses as to labels to assign to respective images of the image collection, the labels corresponding to the guesses identifying people possibly captured in the respective images; receiving feedback that confirms at least one guess in the plurality of guesses; and assigning the second label to the second image based upon the plurality of guesses and the feedback. 11. The method of claim 1, further comprising: extracting a feature from the first image, the feature being indicative of the event captured in the first image, the feature having a value; updating a relational model that represents relationships between events captured in images of the image collection and people captured in images of the image collection; and assigning the second label to the second image based upon the updating of the relational model. 12. A system comprising: a processor; and a memory that comprises a plurality of components that are executed by the processor, the plurality of components comprising: an interface component that causes a graphical user interface to be displayed on a display screen of a computing device, the graphical user interface configured to receive assignations of respective labels to images in an image collection, wherein the interface component receives an assignation of a first label for a first image in the image collection, the first label identifying an event captured in the first image; and an inference component that infers a second label for a second image in the image collection based upon the first label being assigned to the first image, the second label identifying a person captured in the second image, wherein the interface component is configured to output a suggestion that the second label be assigned to the second image. 13. The system of claim 12, the plurality of components further comprising: an assignor component that, responsive to the interface component outputting the suggestion, receives feedback that confirms that the second image captures the person, the assignor component assigns the second label to the second image responsive to receipt of the feedback. 14. The system of claim 12, wherein the plurality of components further comprises: a learning component that is configured to update a relational model based upon the inference component inferring the second label for the second image, the relational model configured to model relationships between people captured in the images of the image collection and events captured in the images of the image collection. 15. The system of claim 12, the plurality of components further comprises: a learning component that is configured to update a relational model based upon the inference component inferring the second label for the second image, the relational model configured to model relationships between events captured in the images of the image collection and locations at which respective images in the image collection were taken. 16. The system of claim 12, the plurality of components further comprises: an extractor component that extracts at least one feature having at least one value from the first image, the at least one feature and the at least one value being indicative of the event, wherein the inference component infers the second label for the second image based upon the at least one feature and the at least one value. 17. The system of claim 12, wherein the interface component receives an assignation of a third label to the first image, the third label identifying a second person captured in the first image, and wherein the inference component infers the second label based upon the third label being assigned to the first image. 18. The system of claim 12, wherein the interface component receives an assignation of a third label to the first image, the third label identifying a location at which the first image was taken, and wherein the inference component infers the second label based upon the third label being assigned to the first image. 19. The system of claim 12, wherein the first label is a name that is descriptive of the event. 20. A computer-readable data storage device comprising instructions that, when executed by a processor, cause the processor to perform acts comprising: causing a graphical user interface to be displayed on a display screen of a computing device, the graphical user interface configured to receive assignations of labels to respective images in an image collection; receiving, by way of the graphical user interface, an assignment of a first label to a first image in the image collection, the label being a name of an event that is captured in the first image; responsive to receiving the assignment of the first label to the first image, extracting a feature from the first image, the feature having a value; inferring a second label to be assigned to a second image in the image collection based upon the value of the feature, the second label identifying a person captured in the second image; displaying, by way of the graphical user interface, a recommendation to assign the second label to the second image; receiving feedback by way of the graphical user interface that confirms the recommendation; and responsive to receiving the feedback, assigning the second label to the second image.