1. A Wrapper-Based Approach to Image Segmentation and Classification Michael E. Farmer, Member, IEEE, and Anil K. Jain, Fellow, IEEE

2. 大綱 Introduction Introduction Overview of the approach Overview of the approach Experiment: Vision-Base airbag suppression Experiment: Vision-Base airbag suppression application application Experimental result Experimental result

3. Introduction

4. Traditional processing The traditional processing flow for image- based pattern recognition consists of image segmentation followed by classification.

5. Three limitations of traditional processing The object of interest “ should be uniform and homogeneous with respect to some characteristic ” and “ adjacent regions should be differing significantly ” There are few metrics available for evaluating segmentation algorithms Inability to adapt to real-world changes

6. The contributions in this paper Developing a closed-loop framework for image segmentation to find the best segmentation for a given class of objects by using the shape of the object for classification of the segmented object Using the probability of correct classification of the object to provide an “ objective evaluation of segmented outputs ” The system can adapt to “ real-world changes. ”

7. Overview of the approach

8. Wrapper-Based Approach Wrap the segmentation and the classification together, and use the classifier as the metric for selecting the best segmentation. Using the classifier to intelligently re-assemble to solve over-segmented problem. The classification is correct when the minimum distance between the classification of the candidate segmentation and one of the desired pattern classes < T

9. Traditional vs Wrapper-Base

10. Experiment: Vision-Base airbag suppression application

11. Problem Infantor Adult

12. Challenges Nonuniform illumination Poor image contrast Shadows and highlights Occlusions Sensor noise Background clutter

13. Variability for the infant class

15. Proposed approach

16. Preliminary Segmentation Reduce the number of blobs that must be processed. Once the correlation value for each region is determined, an adaptive threshold is applied, and any region that falls below the threshold is considered a part of the foreground.

17. Preliminary Segmentation

19. RegionLabeling Region Labeling Using the EM algorithm with a fixed number of components, and then rely on the classification accuracy to determine if more components are required. Merging the very small blobs by mode filter Merging any regions that are smaller then 20 pixels in size with their larger neighbors

20. RegionLabeling Results Region Labeling Results

22. Blob Combiner We have framed the blob combiner problem as one of blob selection, where there exists a subset of blobs that will provide the highest classification accuracy for a given pattern class Forward selection mode Forward selection mode Backward selection mode Backward selection mode

23. Blob Combiner ( Blob Combiner ( plus-L, minus-R algorithm )

25. Feature Extraction

27. Acceleration Methods for Feature Extraction Acceleration Methods for Feature Extraction: Precompute the moments for each blob Compute the moments using only the local neighborhood of each blob. Attain over a ten thousand-fold reduction in processing for each moment calculated.

28. Classification of Blob Combinations Using the nearest neighbor classifier to compute classification distance Feature 1 Feature 2 : class - A points : class - B points : point with unknown class Circle of 1 - nearest neighbor The point is class B via 1-NNR.

29. Proposed approach

30. Demonstrating

33. EXPERIMENTAL RESULTS

35. Correct segmentations

36. Incorrect segmentation