A Wrapper-Based Approach to Image Segmentation and Classification

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A Wrapper-Based Approach to Image Segmentation
and Classification

• Michael E. Farmer, Member, IEEE, and Anil K.
  Jain, Fellow, IEEE

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• Introduction
• Overview of the approach
• Experiment Vision-Base airbag suppression
  
• application
• Experimental result

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Introduction
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Traditional processing

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

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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

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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.

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Overview of the approach
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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

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

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Challenges

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

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Variability for the infant class
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Variability for the infant class
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Proposed approach
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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.

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Preliminary Segmentation
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Preliminary Segmentation
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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

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Region Labeling Results
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Region Labeling Results
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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

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

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Classification of Blob Combinations

• Using the nearest neighbor classifier to compute
  classification distance

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Proposed approach
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Demonstrating
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Demonstrating
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Demonstrating
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EXPERIMENTAL RESULTS
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EXPERIMENTAL RESULTS
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Correct segmentations
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Incorrect segmentation