Normalized Cuts Demo

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Normalized Cuts Demo. Original Implementation from: Jianbo Shi Jitendra Malik Presented by: Joseph Djugash. Outline. Clustering Point The Eigenvectors The Affinity Matrix Comparison with K-means Segmentation of Images The Eigenvectors Comparison with K-means. - PowerPoint PPT Presentation

Transcript of Normalized Cuts Demo

  • Normalized Cuts DemoOriginal Implementation from: Jianbo ShiJitendra Malik

    Presented by:Joseph Djugash

  • OutlineClustering PointThe EigenvectorsThe Affinity MatrixComparison with K-meansSegmentation of ImagesThe EigenvectorsComparison with K-means

  • Clustering How many groups are there?Out of the various possible partitions, which is the correct one?

  • Clustering Why is it hard?Number of components/clusters?

    The structure of the components?

    Estimation or optimization problem?Convergence to the globally correct solution?

  • Clustering Example 1Optimal?How do we arrive at this Clustering?

  • What does the Affinity Matrix Look Like?

  • The Eigenvectors and the ClustersStep-Function like behavior preferred!

    Makes Clustering Easier.

  • The Eigenvectors and the Clusters

  • Clustering Example 2

  • Normalized Cut Result

  • The Affinity Matrix

  • The Eigenvectors and the Clusters

  • K-means Why not?InputEigenvectorsAffinity MatrixEigenvector ProjectionNCut OutputK-means OutputK-means Clustering?Possible but not Investigated Here.

  • K-means Result Example 1

  • K-means Result Example 2

  • Varying the Number of Clustersk = 3k = 4k = 6K-meansN-Cut

  • Varying the Sigma Value = 3 = 13 = 25

  • Image Segmentation Example 1Affinity/Similarity matrix (W) based on Intervening Contours and Image Intensity

  • The Eigenvectors

  • Comparison with K-meansNormalized CutsK-means Segmentation

  • How many Segments?

  • Good Segmentation (k=6,8)

  • Bad Segmentation (k=5,6) Choice of # of Segments in Critical. But Hard to decide without prior knowledge.

  • Varying Sigma (= Too Large)

  • Varying Sigma (= Too Small) Choice of Sigma is important. Brute-force search is not Efficient. The choice is also specific to particular images.

  • Image Segmentation Example 2

  • Image Segmentation Example 2Normalized CutsK-means Segmentation

  • Image Segmentation Example 3

  • Image Segmentation Example 3Normalized CutsK-means Segmentation

  • Image Segmentation Example 4

  • Image Segmentation Example 4Normalized CutsK-means Segmentation

  • Image Segmentation Example 5

  • Image Segmentation Example 5Normalized CutsK-means Segmentation

  • Image Segmentation Example 6

  • Comparison with K-meansNormalized CutsK-means Segmentation

  • The End

  • The Eigenvectors and the ClustersEigenvector #1Eigenvector #2Eigenvector #3Eigenvector #4Eigenvector #5

    Boundary cues are incorporated by looking for the presence of an intervening contour, a large gradient along a straight line connecting two pixels.

    Boundary cues are incorporated by looking for the presence of an intervening contour, a large gradient along a straight line connecting two pixels.