Modular intelligent multimedia analysis system

• US 20020183984A1
• Filed: 06/05/2001
• Published: 12/05/2002
• Est. Priority Date: 06/05/2001
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Tesic, Jelena, Deng, Yining
• Application Number:
  US09/875,434
• Publication Number:
  US 20020183984A1
• Time in Patent Office: Days
• US Class Current: 703/1
• CPC Class Codes: G06F 16/50 of still image data
• CPC Class Codes: G06F 16/58 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

A system and method for categorizing non-textual subject data, such as digital images, utilizes content-based data and meta-data to determine outcomes of classification tasks. The classification system has a modular architecture in which modules configured to perform specific functions, including algorithmic functions, can be integrated or deleted from the system. At the center of the classification system is a decision module comprising: (1) a task component having a number of classification tasks arranged within a task tree configuration, (2) an algorithmic component for selecting an algorithm for each classification task, (3) a sub-algorithmic component for selecting sub-algorithmic routines for each algorithm, and (4) a learning component for constructing and modifying the arrangement of the task tree and the classification tasks based on the frequencies of occurrences for the classes associated with a set of files.

Claims

1. A system for classifying files of non-textual subject data comprising:

a system decision module that includes;

(a) a task component having a plurality of classification tasks arranged in a sequential progression of decision making, said sequential progression of decision making including a plurality of classification nodes for assigning classes, at least some of said classification nodes including algorithms for determining which of a plurality of alternative next classification nodes is to be encountered in said sequential progression of decision making;

(b) an algorithmic component for selecting an algorithm for each of said classification tasks, said algorithm being configured to execute at least one of content-based analysis for processing content-based data and meta-data analysis for processing meta-data;

(c) a sub-algorithmic component for selecting at least one sub-algorithmic routine for said algorithm, said sub-algorithmic routine being selected based on said selecting said algorithm; and

(d) a learning component for modifying said arrangement of classification tasks according to determinations of the frequencies of assignments of said classes to said files of non-textual subject data.

2. The system of claim 1 further comprising a system web-service module for providing Internet access to said system decision module.

3. The system of claim 1 further comprising a system interface module for providing communications among a plurality of system and non-system modules, wherein one of said system modules is said system decision module.

4. The system of claim 3 wherein each of said non-system modules includes at least one said sub-algorithmic routine.

5. The system of claim 3 wherein said system interface module further includes data components for storing data associated with classifying a plurality of said files of said non-textual subject data and at least one control component for executing said sub-algorithmic routines.

6. The system of claim 1 further comprising a media input/output module for administering data associated with classifying said non-textual subject data by reading and writing said data among a plurality of modules.

7. The system of claim 1 wherein said learning component is configured to identify an algorithm for each of said classification tasks and at least one sub-algorithmic routine for said algorithm.

8. The system of claim 1 further comprising a data capturing device configured to capture said content-based data and record said meta-data, said content-based data corresponding to content information of a file of said subject data and said meta-data corresponding to situational environmental data of said data capturing device during a capture of said subject data.

9. A method for categorizing files of non-textual data comprising the steps of:

establishing a sequential progression of decision making, including using automated processing techniques to define a dependent arrangement of a plurality of task nodes, each said task node being associated with a class for classifying a data file, at least some of said task nodes including algorithms for determining which alternative next task node is to be selected in said sequential progression of decision making, said task nodes including multi-algorithmic task nodes having a plurality of alternative said algorithms for implementing said determination;

receiving a file of non-textual subject data; and

progressing said file through said sequential progression of decision making, including selecting from among said alternative algorithms at said multi-algorithmic decision nodes at least partially based on prior determinations at previously encountered task nodes in said sequential progression.

10. The method of claim 9 wherein said step of establishing includes a learning procedure in which content-based data is extracted from each of a plurality of training images and meta-data is identified for each said training image.

11. The method of claim 10 further comprising a step of generating a plurality of learning classes that are descriptive of said training images, including using an association pattern technique, said step of generating including applying content-based analysis for said content-based data and meta-data analysis for said meta-data.

12. The method of claim 9 further comprising a step of dynamically modifying said sequential progression of decision making, including monitoring said determinations at each of said decision nodes and adjusting for detected patterns in said determinations.

13. The method of claim 9 further comprising a step of assigning a semantic description to said file of non-textual subject data for one of organizing said file and matching a query during a search for said file.

15. The method of claim 14 wherein said step of applying includes selecting said algorithmic routine from a plurality of algorithmic routines.

16. The method of claim 14 further comprising a step of defining said algorithmic routine for generating said class based on a training procedure by subjecting a plurality of training image data files having characteristics attributable with said class.

17. The method of claim 14 wherein said step of applying includes a selection of said algorithmic routine at least partially based on a determination of a previous classification task.

18. The method of claim 14 wherein said step of performing said feature analysis includes applying statistical analysis on said transformed image data.

14. A method for identifying a class for a data file at a classification node comprising the steps of:

subjecting an image data file to a transformation function to generate transformed image data, said step of subjecting including transforming at least one of content-based data and meta-data, said content-based data corresponding to image data of said file and said meta-data corresponding to situationally surrounding conditions of a recording device during a capture of said image data file;

performing feature analysis on said transformed image data to derive feature data characteristic of said file; and

applying an algorithmic routine utilizing said feature data to generate a class identifiable with said file.