Method And Apparatus For Automatic Image Categorization

• US 20080089591A1
• Filed: 10/11/2006
• Published: 04/17/2008
• Est. Priority Date: 10/11/2006
• Status: Abandoned Application

• Current Assignee: Seiko Epson Corporation (Seiko Group)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Zhou, Hui, Wong, Alexander Sheung Lai
• Application Number:
  US11/548,377
• Publication Number:
  US 20080089591A1
• Time in Patent Office: Days
• US Class Current: 382/224
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06V 10/758 Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 10/809 of classification results, e.g. where the classifiers operate on the same input data

Abstract

A method of automatically categorizing an input image comprises extracting features of the input image and generating a signature vector representing the input image. The signature vector is processed using diverse classifiers to classify the input image based on the combined output of the diverse classifiers.

Claims

1. A method of automatically categorizing an input image comprising:

extracting features of said input image and generating a signature vector representing said input image based on said extracted features; and

processing the signature vector using diverse classifiers to classify said input image based on the combined output of said diverse classifiers.

2. The method of claim 1 wherein said processing comprises generating weighted outputs using said diverse classifiers and evaluating the weighted outputs to classify said input image.

3. The method of claim 2 wherein the extracted features represent color coherence, edge orientation coherence and texture co-occurrence of said input image.

4. The method of claim 3 wherein said diverse classifiers comprise at least two of K-mean-nearest-neighbour classifiers, perceptron classifiers and back-propagation neural network classifiers.

5. The method of claim 4 wherein said diverse classifiers comprise each of K-mean-nearest-neighbour classifiers, perceptron classifiers and back-propagation neural network classifiers.

6. The method of claim 5 wherein the signature vector is processed in stages, with each stage comprising said diverse classifiers.

7. The method of claim 3 further comprising, prior to said extracting, pre-processing said input image.

8. The method of claim 7 wherein said pre-processing comprises at least one of noise filtering and normalizing said input image.

9. The method of claim 8 wherein said pre-processing comprises both noise filtering and normalizing.

10. The method of claim 2 wherein said extracting comprises:

comparing each pixel of said input image to adjacent pixels; and

populating a hue coherence matrix based on the color coherence of each pixel to its adjacent pixels.

11. The method of claim 10 wherein said extracting further comprises:

comparing each pixel of said input image to adjacent pixels; and

populating an edge orientation coherence matrix based on the edge orientation coherence of each pixel to its adjacent pixels.

12. The method of claim 11 wherein said extracting further comprises:

comparing intensity levels of each adjacent pair of pixels of said input image; and

populating a texture co-occurrence matrix based on the number of times each available pixel intensity level pair occurs in said input image.

13. The method of claim 12 wherein said hue coherence, edge orientation coherence and texture co-occurrence matrices form said signature vector and wherein during said processing, said signature vector is processed by a hierarchical categorization node structure.

14. The method of claim 13 wherein during said processing, each node of said structure, in response to signature vector input, classifies the input image.

15. The method of claim 14 wherein during said processing, each node uses diverse classifiers to process signature vector input and classify the input image.

16. The method of claim 15 wherein during said processing, at each node weighted outputs of the diverse classifiers are used to classify the input image.

17. A categorization system for automatically categorizing an input image comprising:

a signature vector generator extracting features of said input image and generating a signature vector representing said input image based on said extracted features; and

a processing node network processing the signature vector using diverse classifiers to classify said input image based on the combined output of said diverse classifiers.

18. A categorization system according to claim 17 wherein said processing node network uses weighted outputs of said diverse classifiers to classify said input image.

19. A categorization system according to claim 18 wherein said signature vector comprises a hue coherence component, an edge orientation component and a texture co-occurrence component.

20. A categorization system according to claim 19 wherein said signature vector generator compares each pixel of said input image to adjacent pixels and populates a hue coherence matrix based on the color coherence of each pixel to its adjacent pixels thereby to generate said hue coherence component.

21. A categorization system according to claim 20 wherein said signature vector generator compares each pixel of said input image to adjacent pixels and populates an edge orientation coherence matrix based on the edge orientation coherence of each pixel to its adjacent pixels thereby to generate said edge orientation component.

22. A categorization system according to claim 21 wherein said signature vector generator compares intensity levels of each adjacent pair of pixels of said input image and populates a texture co-occurrence matrix based on the number of times each available pixel intensity level pair occurs in said input image thereby to generate said texture co-occurrence component.

23. A categorization system according to claim 22 wherein each node of said network in response to signature vector input classifies the input image.

24. A categorization system according to claim 18 wherein each node of said network comprises diverse classifiers.

25. A categorization system according of claim 24 wherein said diverse classifiers comprise each of K-mean-nearest-neighbour classifiers, perceptron classifiers and back-propagation neural network classifiers.

26. A method of creating a vector set used to train a neural network node comprising:

extracting features from training images;

generating a signature vector for each training image based on said extracted features thereby to create a vector set for said training images; and

adding additional vectors to said vector set based on a subset of said extracted features thereby to create an expanded vector set.

27. The method of claim 26 further comprising:

reducing the expanded vector set based on vector density.

28. The method of claim 27 wherein the additional vectors are added to even vector distribution and to add controlled randomness.