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Color saliency: A goodness measure for color segmentation 10/27/2010

Color saliency: A goodness measure for color segmentation 10/27/2010. Jacob D’Avy. Color saliency. The color saliency measure was intended for use in parameter optimization rather than performance evaluation. The color saliency measure is an unsupervised goodness function.

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Color saliency: A goodness measure for color segmentation 10/27/2010

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  1. Color saliency:A goodness measure for color segmentation 10/27/2010 Jacob D’Avy

  2. Color saliency • The color saliency measure was intended for use in parameter optimization rather than performance evaluation. • The color saliency measure is an unsupervised goodness function. • It assigns a score to a segmentation result based on the color difference between neighboring regions. G. Heidemann , “Region saliency as a measure for colour segmentation stability,” Image and Vision Computing, vol. 26, no. 2, pp. 211-227, 2008.

  3. Color saliency • Definition: the color difference between a region and the surrounding regions sum over all border pixels sum over all pixels in a 4 neighborhood around (x,y) G. Heidemann , “Region saliency as a measure for colour segmentation stability,” Image and Vision Computing, vol. 26, no. 2, pp. 211-227, 2008.

  4. Color saliency • The average saliency S(I) of an image I is the average over all its regions: • Higher S(I) values correspond to “better” segmentations. G. Heidemann , “Region saliency as a measure for colour segmentation stability,” Image and Vision Computing, vol. 26, no. 2, pp. 211-227, 2008.

  5. Outline • Introduction to segmentation parameter optimization • Related work • Pixel pairing • Results

  6. Results Segmentation method: Efficient graph based Parameters: = 360 images Input:

  7. Results input High scores parameters Low scores

  8. Results Segmentation method: Efficient graph based Parameters: = 360 images Input:

  9. Results input High scores parameters Low scores

  10. Results Segmentation method: Efficient graph based Parameters: = 1300 images Input:

  11. Results input High scores parameters Low scores

  12. Observations • Color saliency was used for parameter optimization in [1] with some success. • The results for the test images show a tendency to give higher scores to undersegmented output. • In [2] the color saliency measure is used to optimize parameters for k-means segmentation. The results shown there also seem to favor undersegmentation.

  13. References • G. Heidemann , “Region saliency as a measure for colour segmentation stability,” Image and Vision Computing, vol. 26, no. 2, pp. 211-227, 2008. • D. Ilea and P. Whelan, “Colour Saliency-Based Parameter Optimization for Adaptive Colour Segmentation,” International Conference on Image Processing, 2009.

  14. End

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