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A Wrapper-Based Approach to Image Segmentation and Classification

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Title: A Wrapper-Based Approach to Image Segmentation and Classification Author: SuperXP Last modified by: SuperXP Created Date: 11/20/2006 2:47:49 AM – PowerPoint PPT presentation

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Title: A Wrapper-Based Approach to Image Segmentation and Classification


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

2
??
  • Introduction
  • Overview of the approach
  • Experiment Vision-Base airbag suppression
  • application
  • 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 lt T

9
Traditional vs Wrapper-Base
10
Experiment Vision-Base airbag suppression
application
11
Problem
Infant or Adult

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

13
Variability for the infant class
14
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
18
Preliminary Segmentation
19
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
Region Labeling Results
21
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
  • Backward selection mode

23
Blob Combiner ( plus-L, minus-R algorithm )
24
Blob Combiner ( plus-L, minus-R algorithm )
25
Feature Extraction
26
Feature Extraction
27
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

29
Proposed approach
30
Demonstrating
31
Demonstrating
32
Demonstrating
33
EXPERIMENTAL RESULTS
34
EXPERIMENTAL RESULTS
35
Correct segmentations
36
Incorrect segmentation
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