SYSTEMS AND METHODS FOR USING LATENT VARIABLE MODELING FOR MULTI-MODAL VIDEO INDEXING

• US 20150243276A1
• Filed: 02/27/2014
• Published: 08/27/2015
• Est. Priority Date: 02/27/2014
• Status: Active Grant

• Current Assignee: Fujifilm Business Innovation Corp. (Fujifilm Holdings Corporation)
• Original Assignee: Fuji Xerox Company Limited (Fujifilm Holdings Corporation)
• Inventors: Cooper, Matthew L., Joshi, Dhiraj, Chen, Huizhong
• Granted Patent: US 9,542,934 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/7834 using audio features
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 20/40 in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44
• CPC Class Codes: G06V 20/635 Overlay text, e.g. embedded captions in a TV program
• CPC Class Codes: G10L 15/05 Word boundary detection
• CPC Class Codes: G11B 27/00 Editing; Indexing; Addressing; Timing or synchronising; Monitoring; Measuring tape travel
• CPC Class Codes: G11B 27/28 by using information signals recorded by the same method as the main recording G11B27/22 takes precedence
• CPC Class Codes: H04N 21/234336 by media transcoding, e.g. video is transformed into a slideshow of still pictures or audio is converted into text
• CPC Class Codes: H04N 21/440236 by media transcoding, e.g. video is transformed into a slideshow of still pictures, audio is converted into text
• CPC Class Codes: H04N 7/0882 for the transmission of character code signals, e.g. for teletext circuits for the digital non-picture data signal H04N7/035

Abstract

A computer-implemented method performed in connection with a computerized system incorporating a processing unit and a memory, the computer-implemented method involving: using the processing unit to generate a multi-modal language model for co-occurrence of spoken words and displayed text in the plurality of videos; selecting at least a portion of a first video; extracting a plurality of spoken words from the selected portion of the first video; extracting a first displayed text from the selected portion of the first video; and using the processing unit and the generated multi-modal language model to rank the extracted plurality of spoken words based on probability of occurrence conditioned on the extracted first displayed text.

Claims

1. A computer-implemented method performed in connection with a computerized system comprising a processing unit and a memory, the computer-implemented method comprising:

a. using the processing unit to generate a multi-modal language model for co-occurrence of spoken words in the plurality of videos and an external text associated with the plurality of videos;

b. selecting at least a portion of a first video;

c. extracting a plurality of spoken words from the selected portion of the first video;

d. obtaining a first external text associated with the selected portion of the first video; and

e. using the processing unit and the generated multi-modal language model to rank the extracted plurality of spoken words based on probability of occurrence conditioned on the obtained first external text.

2. The computer-implemented method of claim 1, wherein the obtaining the first external text comprises extracting the first external text from a text displayed in the selected portion of the first video.

3. The computer-implemented method of claim 1, wherein the external text is displayed in at least one of the plurality of videos.

4. The computer-implemented method of claim 1, wherein the external text is contained in a content associated with at least one of the plurality of videos.

5. The computer-implemented method of claim 1, wherein each of the plurality of videos comprises a plurality of presentation slides comprising the external text.

6. The computer-implemented method of claim 1, wherein generating the multi-modal language model comprises extracting all spoken words from all of the plurality of videos and extracting the external text displayed in the plurality of videos and calculating a plurality of probabilities of co-occurrence of each of the extracted spoken words and each of the extracted external text.

7. The computer-implemented method of claim 1, wherein the multi-modal language model is stored in a matrix form.

11. The computer-implemented method of claim 1, further comprising providing the ranked plurality of spoken words to the user;

receiving from the user a selection of at least one of the provided plurality of spoken words; and

using the received selection of the at least one of the provided plurality of spoken words as an annotation for the first video.

12. The computer-implemented method of claim 11, further comprising using the annotation to index at least some of the plurality of videos.

13. The computer-implemented method of claim 1, further comprising using the ranked extracted plurality of spoken words to index at least some of the plurality of videos.

14. The computer-implemented method of claim 1, further comprising using top ranked words from the ranked extracted plurality of spoken words to index at least some of the plurality of videos.

8. The computer-implemented method of 1, wherein the plurality of spoken words is extracted from the selected portion of the first video using automated speech recognition (ASR).

9. The computer-implemented method of 1, wherein the plurality of spoken words are extracted from the selected portion of the first video using close captioning (CC) information associated with the first video.

10. The computer-implemented method of 1, wherein obtaining the first external text associated with the selected portion of the first video comprises detecting slides in the selected portion of the first video and extracting the first external text from the detected slides using optical character recognition (OCR).

15. The computer-implemented method of 1, wherein the extracted plurality of spoken words comprise a phrase.

16. The computer-implemented method of 1, wherein the extracted plurality of spoken words comprise a sentence.

17. The computer-implemented method of 1, wherein the selected portion of the first video comprises a contextually meaningful segment of the first video.

18. A non-transitory computer-readable medium embodying a set of computer-executable instructions, which, when executed in connection with a computerized system comprising a processing unit and a memory, cause the computerized system to perform a method comprising:

a. using the processing unit to generate a multi-modal language model for co-occurrence of spoken words in the plurality of videos and an external text associated with the plurality of videos;

b. selecting at least a portion of a first video;

c. extracting a plurality of spoken words from the selected portion of the first video;

d. obtaining a first external text associated with the selected portion of the first video; and

e. using the processing unit and the generated multi-modal language model to rank the extracted plurality of spoken words based on probability of occurrence conditioned on the obtained first external text.

19. A computerized system comprising a processing unit and a memory storing a set of instructions, the set of instructions comprising instructions for:

a. using the processing unit to generate a multi-modal language model for co-occurrence of spoken words in the plurality of videos and an external text associated with the plurality of videos;

b. selecting at least a portion of a first video;

c. extracting a plurality of spoken words from the selected portion of the first video;

d. obtaining a first external text associated with the selected portion of the first video; and

e. using the processing unit and the generated multi-modal language model to rank the extracted plurality of spoken words based on probability of occurrence conditioned on the obtained first external text.