On Morphological Color

1
On Morphological Color Texture Characterization
Erchan Aptoula and Sébastien Lefèvre
Image Sciences, Computer Sciences and Remote
Sensing Laboratory Louis Pasteur
University Strasbourg, France
aptoula,lefevre_at_lsiit.u-strasbg.fr
October 12, 2007 ISMM
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Contents

• Morphological tools for texture analysis
• Granulometry covariance
• A combination of SE size-direction-distance
• Implementation on color images
• Application results

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Morphological texture description

• Texture characteristics regularity
  (periodicity),
• directionality, complexity, overall color and
  color purity

• A rich variety of tools granulometry,
  morphological covariance-variogram, orientation
  maps, etc

• Main advantage of morphological approaches
  their inherent capacity to exploit spatial pixel
  relations

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Granulometry

• Standard granulometry of an image f

• Extracts information on the granularity of its
  input

• Has several extensions attribute based,
  multivariate, spatial, etc

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Morphological covariance

• Morphological covariance of an image f

P2,v a pair of points separated by a vector v

• Extracts information on the regularity,
  directionality and coarseness of its input

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Covariance granulometry ?

• Granulometry
• granularity

• Covariance
• regularity
• directionality
• coarseness

• They extract complementary information, how
  should they be combined, by concatenation, ... or?

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Covariance granulometry ?

• Employ 3 structuring element variables size,
  direction and distance.

Size granularity
Distance regularity
Direction directionality
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Covariance granulometry
P?,v a pair of SEs of size ?, separated by a
vector v

• However SE is not convex pseudo
  granulometry

Strongly ordered texture
Disordered texture
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Extending to color images

• Requirements
• A suitable color space
• A color ordering scheme (preferably total), to
  impose a lattice structure

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Color space choice

• Color space choice perceptual, polar, etc...

• Polar color spaces () intuitive components

(-) manipulation of hue
(-) multiple implementations
Luminance
Saturation
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Color ordering

• Luminance contains the majority of variational
  information

• Color auxiliary component

• For which levels of luminance does color become
  more important ?

2. How should the balance between luminance and
color use be determined ?
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Color ordering

• For which levels of luminance does color become
  more important ?

a
b
c
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Color ordering
2. How should the balance between luminance and
color use be determined ?

• Image or vector specific configurations are
  better suited for intra-image applications

• Here, an image database specific approach is
  used, by means of genetic optimization

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Application

• Outex13 texture database 1360 images (128 x
  128) of 68 colour textures

• Four directions (0,45,90,135), 15 different
  SE sizes
• (k1 to 30, 2k1) and 20 distances

• Results in a feature of size 20x4x15, which was
  reduced to 20x4x2 by PCA

• kNN classifier (k1) and the
• Euclidean distance

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Classification accuracies
Optimized Color
Color
Grayscale
Features
Granulometry Covariance Concatenated Combined
67.53 73.82 77.75 83.53
68.78 76.92 79.93 85.49
72.03 80.46 83.74 88.13
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Conclusion and perspectives

• A way of combining the complementary information
  provided by granulometry and covariance

• However, it leads to a pseudo granulometry

• Genetic optimization aids in exploiting color

• Shape variations, as well as the role of hue
  remain to be investigated

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Thank you for your attention
E. Aptoula and S. Lefèvre
aptoula,lefevre_at_lsiit.u-strasbg.fr