Method and system for generating concept-specific data representation for multi-concept detection

• US 20050289179A1
• Filed: 06/23/2004
• Published: 12/29/2005
• Est. Priority Date: 06/23/2004
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Smith, John R., Naphade, Milind R., Natsev, Apostol Ivanov
• Application Number:
  US10/874,553
• Publication Number:
  US 20050289179A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• 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

Abstract

A system and method for detecting a concept from digital content are provided. A plurality of representations is generated for same data content for concept detection from the plurality of representations. A plurality of concepts is simultaneously detected from the plurality of representations of the same data content wherein at least one detector provides selection information for selecting the representations generated or a combination of the generated representations. This results in multiple instances of a representation being considered for concept detection.

Claims

1. A method for detecting a concept from digital content, comprising the steps of:

generating a plurality of representations for same data content for concept detection from the plurality of representations; and

simultaneously detecting a plurality of concepts from the plurality of representations of the same data content wherein at least one detector provides selection information for selecting at least one of the representations generated or a combination of the representations.

2. The method as recited in claim 1, wherein the step of generating a plurality of representations includes generating one or more of a color-based representation, a layout-based representation, a texture-based representation and a grid-based representation.

3. The method as recited in claim 1, wherein the plurality of representations includes redundant content.

4. The method as recited in claim 1, wherein the step of generating includes selecting one or more representations from the plurality of representations.

5. The method as recited in claim 1, wherein the step of generating includes combining representations from the plurality of representations to create a representation suitable for concept detection.

6. The method as recited in claim 1, wherein the step of generating includes generating the plurality of representations independent of a process employed for generating a given representation for input content.

7. The method as recited in claim 6, wherein the step of generating includes changing the process employed for generating a given representation for input content.

8. The method as recited in claim 1, further comprising the step of determining confidence scores for each concept from the plurality of representations.

9. The method as recited in claim 1, further comprising the step of outputting a maximum confidence for a concept in one representation.

10. The method as recited in claim 1, wherein the step of detecting includes employing concept models to determine if the concept is present in a representation.

11. The method as recited in claim 1, further comprising the step of tuning a representation to provide an improved representation for concept detection.

12. The method as recited in claim 11, wherein the step of tuning includes adjusting representation generation parameters to provide the improved representation for concept detection.

13. The method as recited in claim 11, wherein the step of adjusting includes updating at least one parameter from a repository including associations between concept labels and representation creation procedures.

14. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for detecting a concept from digital content, as recited in claim 1.

15. A method for detecting a concept from digital content, comprising the steps of:

providing digital content;

representing the digital content in a plurality of representations;

generating a set of regions for each of the plurality of representations for the same data content;

simultaneously detecting a plurality of concepts from the regions;

scoring each region based on confidence that the concepts exist in each region; and

processing region scores.

16. The method as recited in claim 15, wherein the step of representing includes generating one or more of a color-based representation, a layout-based representation, a texture-based representation and a grid-based representation.

17. The method as recited in claim 15, wherein the plurality of representations includes redundant content.

18. The method as recited in claim 15, wherein the step of generating includes combining representations to create a representation suitable for concept detection.

19. The method as recited in claim 15, wherein the step of generating includes generating the plurality of representations independent of a process employed for generating a given representation for input content.

20. The method as recited in claim 15, wherein the step of detecting includes employing concept models to determine if the concept is present in the representation.

21. The method as recited in claim 15, further comprising the step of tuning a representation to provide an improved representation for concept detection.

22. The method as recited in claim 21, wherein the step of tuning includes adjusting representation generation parameters to provide the improved representation for concept detection.

23. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for detecting a concept from digital content, as recited in claim 15.

24. A system for detecting a concept from digital content, comprising:

a representation generation module which represents digital content in a plurality of representations by generating a set of regions for each of the plurality of representations for the same data content; and

at least one concept detector which simultaneously detects a plurality of concepts from the regions by comparing data in the region to concept models and scoring each region based on confidence that the concept exists in that region.

25. The system as recited in claim 24, further comprising a combiner, which combines representations to create a representation suitable for concept detection.

26. The system as recited in claim 24, further comprising a representation tuner to provide an improved representation for concept detection by adjusting representation generation parameters to provide the improved representation.

27. The system as recited in claim 24, wherein the parameters are included in a repository, which includes associations between concept labels and representation creation procedures.

28. The system as recited in claim 24, further comprising a score processing module, which processes the region scores generated for each concept from the plurality of representations to create an overall confidence score for each concept.