Optimizing a visual perspective of media

• US 9,881,222 B2
• Filed: 03/20/2017
• Issued: 01/30/2018
• Est. Priority Date: 09/30/2014
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Tumanov, Ilya, Lee, David Benjamin, Halberstam, Jennifer Michelstein, Freier, Nathaniel George, Farouki, Karim T.
• Primary Examiner(s): Liu, Li
• Application Number:
  US15/464,157
• Publication Number:
  US 20170193318A1
• Time in Patent Office: 316 Days
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2207/10004 Still image; Photographic image
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models

Abstract

One or more signals are used to identify regions of interest of an image. The signals are applied to the image to generate one or more models that are based on the regions of interest. The models may present different perspectives of the image by emphasizing various features and focal points. The models may be ranked and displayed according to a scoring paradigm that is based on one or more signals. Multi-tiered feedback mechanisms allow for the collection of user intent and/or other forms of explicit input. Feedback associated to the models may be obtained and used to generate additional models that are based on one or more signals and the feedback. The feedback may also be stored and utilized for machine learning purposes.

Claims

1. A method comprising:

obtaining, by a processor, data defining an intended use of an image;

determining, by the processor, a plurality of salient regions of the image by applying a plurality of signals to the image;

determining, by the processor, a confidence score for at least one salient region of the plurality of salient regions, wherein the confidence score is based at least in part on at least one of a size of an identifiable object in the image, a depth of a color or a luminance variation of the image, an existence of identifiable features of the identifiable object, or an image quality of the image;

selecting, by the processor, the at least one salient region based at least in part on the intended use and the confidence score for the at least one salient region; and

generating, by the processor, a plurality of models, wherein an individual model of the plurality of models brings focus, via color modification, to one or more elements of the at least one salient region of the image differently than other models of the plurality of models bring focus to the one or more elements.

2. The method of claim 1, wherein:

the plurality of salient regions comprises one or more invariant regions; and

the at least one salient region comprises at least one of the one or more invariant regions.

3. The method of claim 1, further comprising:

generating individual models of the plurality of models by applying one or more of the plurality of signals to the at least one salient region of the image;

determining individual scores for the individual models of the plurality of models;

filtering the plurality of models based on the individual scores to determine selected models; and

displaying a transformation of the selected models, wherein an order of the selected models displayed is based on corresponding individual scores.

4. The method of claim 1, further comprising:

displaying a transformation of the individual model of the plurality of models; and

receiving a selection of a single model of the plurality of models.

5. The method of claim 1, further comprising:

displaying a transformation of the individual model of the plurality of models on an interface;

receiving, at the interface, a gesture input indicating an intent directed toward an element in the at least one salient region of the image; and

in response to the gesture input, generating additional models based at least in part on the intent.

6. The method of claim 1, further comprising:

displaying a transformation of the individual model of the plurality of models;

receiving an edit parameter for the image; and

generating an edited model based at least in part on the edit parameter.

7. The method of claim 1, wherein the intended use of the image is based at least in part on a type of device on which the image is to be displayed.

8. A device, comprising:

a processor; and

a computer-readable storage medium in communication with the processor, the computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the device to;

obtain data defining an intended use of an image;

determine a plurality of salient regions of the image by applying a plurality of signals to the image;

determine a confidence score for at least one salient region of the plurality of salient regions, wherein the confidence score is based at least in part on at least one of a size of an identifiable object in the image, a depth of a color or a luminance variation of the image, an existence of identifiable features of the identifiable object, or an image quality of the image;

select the at least one salient region based at least in part on the intended use and the confidence score for the at least one salient region; and

generate a plurality of models, wherein an individual model of the plurality of models brings focus, via color modification, to one or more elements of the at least one salient region of the image differently than other models of the plurality of models bring focus to the one or more elements.

9. The device of claim 8, wherein:

the plurality of salient regions comprises one or more invariant regions; and

the at least one salient region comprises at least one of the one or more invariant regions.

10. The device of claim 8, wherein the computer-readable storage medium has further computer-executable instructions stored thereupon, which when executed by the processor, cause the device to:

generate individual models of the plurality of models by applying one or more of the plurality of signals to the at least one salient region of the image;

determine individual scores for the individual models of the plurality of models;

filter the plurality of models based on the individual scores to determine selected models; and

display a transformation of the selected models, wherein an order of the selected models displayed is based on corresponding individual scores.

11. The device of claim 8, wherein the computer-readable storage medium has further computer-executable instructions stored thereupon, which when executed by the processor, cause the device to:

display a transformation of the individual model of the plurality of models; and

receive a selection of a single model of the plurality of models.

12. The device of claim 8, wherein the computer-readable storage medium has further computer-executable instructions stored thereupon, which when executed by the processor, cause the device to:

display a transformation of the individual model of the plurality of models on an interface;

receive, at the interface, a gesture input indicating an intent directed toward an element in the at least one salient region of the image; and

in response to the gesture input, generate additional models based at least in part on the intent.

13. The device of claim 8, wherein the computer-readable storage medium has further computer-executable instructions stored thereupon, which when executed by the processor, cause the device to:

display a transformation of the individual model of the plurality of models;

receive an edit parameter for the image; and

generate an edited model based at least in part on the edit parameter.

14. The device of claim 8, wherein the intended use of the image is based at least in part on a type of the device.

15. A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a device, cause the device to:

obtain data defining an intended use of an image;

determine a plurality of salient regions of the image by applying a plurality of signals to the image;

determine a confidence score for at least one salient region of the plurality of salient regions, wherein the confidence score is based at least in part on at least one of a size of an identifiable object in the image, a depth of a color or a luminance variation of the image, an existence of identifiable features of the identifiable object, or an image quality of the image;

select the at least one salient region based at least in part on the intended use and the confidence score for the at least one salient region; and

generate a plurality of models, wherein an individual model of the plurality of models brings focus, via color modification, to one or more elements of the at least one salient region of the image differently than other models of the plurality of models bring focus to the one or more elements.

16. The computer-readable storage medium of claim 15, wherein:

the plurality of salient regions comprises one or more invariant regions; and

the at least one salient region comprises at least one of the one or more invariant regions.

17. The computer-readable storage medium of claim 15, wherein the computer-readable storage medium comprises further computer-executable instructions stored thereupon for causing the device to:

display a transformation of the individual model of the plurality of models on an interface;

receive, at the interface, a gesture input indicating an intent directed toward an element in the at least one salient region of the image; and

in response to the gesture input, generate additional models based at least in part on the intent.

18. The computer-readable storage medium of claim 15, wherein the intended use of the image is based at least in part on a type of the device.