Modification of images and associated text

• US 9,996,556 B2
• Filed: 06/07/2017
• Issued: 06/12/2018
• Est. Priority Date: 09/29/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Shioya, Tomohiro, Tada, Masami, Watanabe, Shoichiro
• Primary Examiner(s): Vu, Kim
• Assistant Examiner(s): Delaney, Molly
• Application Number:
  US15/616,555
• Publication Number:
  US 20170270142A1
• Time in Patent Office: 370 Days
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/5854 using shape and object relationship
• CPC Class Codes: G06F 16/5866 using information manually generated, e.g. tags, keywords, comments, manually generated location and time information
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06V 20/20 in augmented reality scenes
• CPC Class Codes: G06V 20/70 Labelling scene content, e.g. deriving syntactic or semantic representations

Abstract

A computer-implemented method includes receiving an image. The image includes one or more objects and one or more text portions. The computer-implemented method further includes identifying the one or more objects. The computer-implemented method further includes, for each of the one or more objects identified, extracting an object tag. The computer-implemented method further includes, for each of the one or more text portions, extracting a text tag. The computer-implemented method further includes, for each text tag, determining whether the text tag describes any of the one or more objects based on the object tag extracted from each object to yield a determination. The computer-implemented method further includes, responsive to the determination: performing an image process to that of the one or more objects, and performing a text process to that of the one or more text portions. A corresponding computer program product and computer system are also disclosed.

Claims

1. A computer-implemented method comprising:

performing a text process to an image, wherein the text process comprises;

assigning a type of speech part to one or more objects included in an image, wherein said type of speech part assigned to each of said one or more objects is based, at least in part, on a selection sequence of said one or more objects; and

generating text corresponding to each of said one or more objects selected based, at least in part, on said type of speech part assigned to each of said one or more objects.

2. The computer-implemented method of claim 1, wherein generating text corresponding to each of said one or more objects selected is further based on contextual information associated with each of said one or more objects.

3. The computer-implemented method of claim 1, further comprising:

identifying said one or more objects included in said image; and

extracting an object tag for each object of said one or more objects.

4. The computer-implemented method of claim 3, wherein generating text corresponding to each of said one or more objects selected is further based on said object tag extracted for each object.

5. The computer-implemented method of claim 3, wherein each object tag extracted for said one or more objects is displayed in a tag list.

6. The computer-implemented method of claim 5, further comprising performing a first image process on a first object included in said image based, at least in part, on a selection of a first object tag displayed in said tag list.

7. The computer-implemented method of claim 1, further comprising performing a second image process on a second object included in said image based, at least in part, on a selection of said second object.

8. The computer-implemented method of claim 7, wherein performing said second image process is further based, at least in part, on contextual information associated with said second object.

9. The computer-implemented method of claim 7, wherein performing said second image process is further based, at least in part, on historical selection data associated with said second object.