Pixel Grouping in Image

DBSCAN Density-Based algorithm for clustering IDX = DBSCAN(X, EPSILON, MINPTS) partitions the points in the N-by-P data matrix X into clusters based on parameters EPSILON and MINPTS. EPSILON is a threshold for a neighborhood search query. MINPTS is a positive integer used as a threshold to determine whether a point is a core point. IDX is an N-by-1 vector containing cluster indices. An index equal to '-1' implies a noise point. IDX = DBSCAN(D, EPSILON, MINPTS, 'DISTANCE', 'PRECOMPUTED') is an alternative syntax that accepts distances D between pairs of observations instead of raw data. D may be a vector or matrix as computed by PDIST or PDIST2, or a more general dissimilarity vector or matrix conforming to the output format of PDIST or PDIST2. [IDX, COREPTS] = DBSCAN(...) returns a logical vector COREPTS indicating indices of core-points as identified by DBSCAN. IDX = DBSCAN(..., 'PARAM1',val1, 'PARAM2',val2, ...) specifies optional parameter name/value pairs to control the algorithm used by DBSCAN. Parameters are: 'Distance' - a distance metric which can be any of the distance measures accepted by the PDIST2 function. The default is 'euclidean'. For more information on PDIST2 and available distances, type HELP PDIST2. An additional choice is: 'precomputed' - Needs to be specified when a custom distance matrix is passed in 'P' - A positive scalar indicating the exponent of Minkowski distance. This argument is only valid when 'Distance' is 'minkowski'. Default is 2. 'Cov' - A positive definite matrix indicating the covariance matrix when computing the Mahalanobis distance. This argument is only valid when 'Distance' is 'mahalanobis'. Default is NANCOV(X). 'Scale' - A vector S containing non-negative values, with length equal to the number of columns in X. Each coordinate difference between X and a query point is scaled by the corresponding element of S. This argument is only valid when 'Distance' is 'seuclidean'. Default is NANSTD(X). Example: % Find clusters in data X, using the default distance metric % 'euclidean'. X = [rand(20,2)+2; rand(20,2)]; idx = dbscan(X,0.5,2); See also KMEANS, KMEDOIDS, PDIST2, PDIST. Documentation for dbscan doc dbscan