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Segmentation Graph-Theoretic Clustering Outline Graph theory basics Eigenvector methods for segmentation Graph Theory Terminology Graph G: Set of vertices V and edges E connecting pairs of vertices Each edge is represented by the vertices (a, b) it joins A weighted graph has a weight associated with each edge w(a, b) Connectivity Vertices are connected if there is a sequence of edges joining them A graph is connected if all vertices are connected Any graph can be partitioned into connected components (CC) such that each CC is a connected graph and there are no edges between vertices in different CCs Graphs for Clustering Tokens are vertices Weights on edges proportional to token similarity Cut: “Weight” of edges joining two sets of vertices: Segmentation: Look for minimum cut in graph Recursively cut components until regions uniform enough A B Representing Graphs As Matrices Use N x N matrix W for N–vertex graph Entry W(i, j) is weight on edge between vertices i and j Undirected graphs have symmetric weight matrices 5 4 9 2 6 1 1 8 1 3 7 from Forsyth & Ponce Example graph and its weight matrix Affinity Measures Affinity A(i, j) between tokens i and j should be proportional to similarity Based on metric on some visual feature(s) T 2 Position: E.g., A(i, j) = exp [-((x-y) (x-y)/2sd )] Intensity Color Texture These are weights in an affinity graph A over tokens Affinity by distance Choice of Scale s s=0.1 s=0.2 s=1 Eigenvectors and Segmentation Given k tokens with affinities defined by A, want partition into c clusters For a particular cluster n, denote the membership weights of the tokens with the vector w n Require normalized weights so that “Best” assignment of tokens to cluster n is achieved by selecting wn that maximizes objective function (highest intra-cluster affinity) subject to weight vector normalization constraint Using method of Lagrange multipliers, this yields system of equations which means that w n is an eigenvector of A and a solution is obtained from the eigenvector with the largest eigenvalue Eigenvectors and Segmentation Note that an appropriate rearrangement of affinity matrix leads to block structure indicating clusters 5 4 9 2 6 1 1 8 1 3 7 from Forsyth & Ponce Largest eigenvectors A of tend to correspond to eigenvectors of blocks So interpret biggest c eigenvectors as cluster membership weight vectors Quantize weights to 0 or 1 to make memberships definite Example using dataset Fig 14.18 Next 3 Eigenvectors Number of Clusters Potential Problem Normalized Cuts Previous approach doesn’t work when eigenvalues of blocks are similar Just using within-cluster similarity doesn’t account for between-cluster differences No encouragement of larger cluster sizes Define association between vertex subset A and full set V as Before, we just maximized assoc(A, A); now we also want to minimize assoc(A, V). Define the normalized cut as Normalized Cut Algorithm Define diagonal degree matrix D(i, i) = Sj A(i, j) Define integer membership vector x over all vertices such that each element is 1 if the vertex belongs to cluster A and -1 if it belongs to B (i.e., just two clusters) Define real approximation to x as This yields the following objective function to minimize: which sets up the system of equations The eigenvector with second smallest eigenvalue is the solution (smallest always 0) Continue partitioning clusters if normcut is over some threshold Example: Fig 14.23 Example: Fig. 14-24

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posted: | 10/8/2012 |

language: | English |

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