Segmentation in color space using clustering

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Segmentation in color space using clustering

• Student Yijian Yang
• Advisor Longin Jan Latecki

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Index

• Introduction
• Segmentation
• Color Space
• Segmentation using both color space and spatial
  space information
• Basic concept
• DBScan algorithm
• Result

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Segmentation

• Definition
• A process of separating of individual
  perception of the scene.
• Importance
• The first step in image processing.

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Segmentation Techniques

• Histogram thresholding
• Assumption The image is composed of regions
  with different color ranges.
• Edge-based approach
• Difficulty Need for joining separated edges.
• Region-based approach
• Region growing
• Region splitting
• Region merging
• Their combination

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Color Space

• There are many different existing color space
• Example RGB, YUV, HSV, Munsell system and so
  on.
• Munsell color system is the best in simulating
  the human color vision.
• Munsell Hue, Munsell Value, Munsell Chroma

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RGB to HVC
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Basic Concept(1)

• Distance

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Basic Concept (2)

• Eps of a point
• The neighborhood within a radius of a given
  point.
• Core point
• If the Eps of a point contains at least a
  minimum number, MinPts, of points with similar
  property, it is a core point.

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Basic Concept(3)

• Directly density-reachable
• Point p is directly density-reachable from a
  point q if
• It is obviously, directly density-reachable is
  symmetric for pairs of core point. But in
  general, it is not symmetric if one core point
  and one border point are involved.

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Basic Concept(4)

• Density-reachable
• A point p is density-reachable from a point q
  wrt. Eps and MinPts if there is a chain of points
  p1, p2, , pn, p1q pnq such that pi1 is
  directly density-reachable from pi.

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Basic Concept(5)

• Density-connected
• A point p is density-connected to a point q wrt.
  Eps and MinPts if there is a point o such that
  both p and q are density-reachable from o wrt.
  Eps MinPts.

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Density-based Algorithm

• Search the unlabeled pixels in an image in order
  of current core pixel and current core region.
• If a core pixel p is found, a new cluster is
  created. Then we iteratively collect unlabeled
  pixels that are density-connected with p, and
  label these pixels with same cluster label.
• If there are still existing core pixels in the
  image, goto 2.
• For the pixels that are not included in any
  clusters, merge them with the cluster that is
  adjacent to them and has the highest similarity
  in average color value with them
• Label each cluster we find in the image as a
  segmentation region.

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Spatial Eps and Minpts

• Spatial Eps
• Simply select the size of Spatial Eps circle
  according to the size of image. The larger the
  image is , the larger its Spatial Eps circle will
  be.
• MinPts
• MinPts is set to half of the number of pixels
  in the Spatial Eps circle, which means that we
  will consider the pixels whose color are same as
  the dominant color in the circle.

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Color space Eps

• To determine the value of Hradius, Vradius, and
  Cradius.

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Program List

• Segmentation Main function
• RGB2HVC Transformation function from RGB to HVC
• Get_radius Calculate H, V, C radius
• ExpandCluster Region growing method
• Region Query Finding homogeneous pixels
• Color_distance Calculate HVC color space
  distance
• Spatial_distance Calculate spatial space
  distance
• ChangeAllId Change label function
• Show_segmentation Display the resulting image
  according to different label.

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Result 1

• Original Picture Segmented Pictures

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Result 2

• Original Picture Segmented Pictures

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Result 3

• Original Picture Segmented Pictures

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Result 4

• Original Picture Segmented Pictures

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Reference

• Color image segmentation using density-based
  clustering
• A density-based algorithm for discovering
  clusters in large spatial databases with noise
• Density-based clustering in spatial databases
  the algorithm GDBSCAN and its application
• Color space analysis and color image segmentation
• Color segmentation techniques

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Thanks!