Using attributes for identifying imagery for selection
A system includes a computing device that includes a memory configured to store instructions. The system also includes a processor to execute the instructions to perform operations that include receiving data representing an image, the image being represented in the data by a collection of visual elements. Operations also include determining whether to select the image for presentation by one or more entities using a machine learning system, the machine learning system being trained using data representing a plurality of training images and data representing one or more attributes regarding image presentation by the one or more entities.
1. A computing device implemented method comprising:
receiving data representing an image, the image being represented in the data by a collection of visual elements; and
determining whether to select the image for presentation by one or more entities using a machine learning system, the machine learning system being trained using at least one of data representing a plurality of training images and data representing one or more attributes regarding image presentation by the one or more entities, wherein the one or more attributes regarding image presentation by the one or more entities is selectable for content inclusion or exclusion by the one or more entities to present the selected image for training the machine learning system.
2. The computing device implemented method of claim 1 , wherein the one or more attributes represent whether each of the training images was presented by one or more of the entities.
3. The computing device implemented method of claim 1 , wherein the one or more attributes represent one or more graphical attributes and one or more content attributes.
4. The computing device implemented method of claim 3 , wherein the one or more attributes are entered into a webpage by the one or more entities.
5. The computing device implemented method of claim 3 , wherein the graphical attributes represent one or more colors included in a corresponding image of the plurality of training images.
6. The computing device implemented method of claim 3 , wherein the content attributes represent whether textual content is present.
7. The computing device implemented method of claim 3 , wherein the content attributes represent whether a particular item is present.
8. The computing device implemented method of claim 1 , wherein the machine learning system provides data representing the one or more attributes of the image.
9. The computing device implemented method of claim 1 , wherein the machine learning system provides data representing whether the image is selectable for presentation by each of the one or more entities.
10. The computing device implemented method of claim 1 , wherein at least one of the training images includes two or more of the attributes.
11. The computing device implemented method of claim 1 , wherein at least one of the training images is absent all of the one or more attributes.
12. The computing device implemented method of claim 1 , wherein one of the training images represents one attribute and another training image represents another attribute.
13. The computing device implemented method of claim 1 , wherein a portion of the training images have been previously rejected.
14. The computing device implemented method of claim 1 , wherein a portion of the training images have been previously published.
15. The computing device implemented method of claim 1 , wherein the machine learning system is also trained on performance data.
16. The computing device implemented method of claim 1 , wherein the machine learning system provides data representing the predicted performance of the image.
17. The computing device implemented method of claim 1 , further comprising:
further training of the machine learning system using data associated with another image selected for publication.
18. A system comprising:
a computing device comprising:
a memory configured to store instructions; and
a processor to execute the instructions to perform operations comprising:
receiving data representing an image, the image being represented in the data by a collection of visual elements; and
determining whether to select the image for presentation by one or more entities using a machine learning system, the machine learning system being trained using at least one of data representing a plurality of training images and data representing one or more attributes regarding image presentation by the one or more entities, wherein the one or more attributes regarding image presentation by the one or more entities is selectable for content inclusion or exclusion by the one or more entities to present the selected image for training the machine learning system.
19. The system of claim 18 , wherein the one or more attributes represent whether each of the training images was presented by one or more of the entities.
20. The system of claim 18 , wherein the one or more attributes represent one or more graphical attributes and one or more content attributes.
21. The system of claim 20 , wherein the one or more attributes are entered into a webpage by the one or more entities.
22. The system of claim 20 , wherein the graphical attributes represent one or more colors included in a corresponding image of the plurality of training images.
23. The system of claim 20 , wherein the content attributes represent whether textual content is present.
24. The system of claim 20 , wherein the content attributes represent whether a particular item is present.
25. The system of claim 18 , wherein the machine learning system provides data representing the one or more attributes of the image.
26. The system of claim 18 , wherein the machine learning system provides data representing whether the image is selectable for presentation by each of the one or more entities.
27. The system of claim 18 , wherein at least one of the training images includes two or more of the attributes.
28. The system of claim 18 , wherein at least one of the training images is absent all of the one or more attributes.
29. The system of claim 18 , wherein one of the training images represents one attribute and another training image represents another attribute.
30. The system of claim 18 , wherein a portion of the training images have been previously rejected.
31. The system of claim 18 , wherein a portion of the training images have been previously published.
32. The system of claim 18 , wherein the machine learning system is also trained on performance data.
33. The system of claim 18 , wherein the machine learning system provides data representing the predicted performance of the image.
34. The system of claim 18 , operations further comprising:
further training of the machine learning system using data associated with another image selected for publication.
35. One or more non-transitory computer readable media storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising:
receiving data representing an image, the image being represented in the data by a collection of visual elements; and
determining whether to select the image for presentation by one or more entities using a machine learning system, the machine learning system being trained using at least one of data representing a plurality of training images and data representing one or more attributes regarding image presentation by the one or more entities, wherein the one or more attributes regarding image presentation by the one or more entities is selectable for content inclusion or exclusion by the one or more entities to present the selected image for training the machine learning system.
36. The non-transitory computer readable media of claim 35 , wherein the one or more attributes represent whether each of the training images was presented by one or more of the entities.
37. The non-transitory computer readable media of claim 35 , wherein the one or more attributes represent one or more graphical attributes and one or more content attributes.
38. The non-transitory computer readable media of claim 37 , wherein the one or more attributes are entered into a webpage by the one or more entities.
39. The non-transitory computer readable media of claim 37 , wherein the graphical attributes represent one or more colors included in a corresponding image of the plurality of training images.
40. The non-transitory computer readable media of claim 37 , wherein the content attributes represent whether textual content is present.
41. The non-transitory computer readable media of claim 37 , wherein the content attributes represent whether a particular item is present.
42. The non-transitory computer readable media of claim 35 , wherein the machine learning system provides data representing the one or more attributes of the image.
43. The non-transitory computer readable media of claim 35 , wherein the machine learning system provides data representing whether the image is selectable for presentation by each of the one or more entities.
44. The non-transitory computer readable media of claim 35 , wherein at least one of the training images includes two or more of the attributes.
45. The non-transitory computer readable media of claim 35 , wherein at least one of the training images is absent all of the one or more attributes.
46. The non-transitory computer readable media of claim 35 , wherein one of the training images represents one attribute and another training image represents another attribute.
47. The non-transitory computer readable media of claim 35 , wherein a portion of the training images have been previously rejected.
48. The non-transitory computer readable media of claim 35 , wherein a portion of the training images have been previously published.
49. The non-transitory computer readable media of claim 35 , wherein the machine learning system is also trained on performance data.
50. The non-transitory computer readable media of claim 35 , wherein the machine learning system provides data representing the predicted performance of the image.
51. The non-transitory computer readable media of claim 35 , operations further comprising:
further training of the machine learning system using data associated with another image selected for publication.