KMeans Segmentation - MEX
KMEANSK - mex implementation (compile by mex kmeansK.cpp
Also an equivalent MATLAB implementation is present in zip file
Performs K-means clustering given a list of feature vectors and k. The argument k indicates the number of clusters you want the data to be divided into. data_vecs (N*R) is the set of R dimensional feature vectors for N data points. Each row in data_vecs gives the R dimensional vector for a single data point. Each column in data_vecs refers to values for a particular feature vector for all the N data points. The output data_idxs is a N*1 vector of integers telling which cluster number a particular data point belongs to. It also outputs centroids which is a k*R matrix, where each rows gives the vector for the cluster center. If we want to segment a color image i into 5 clusters using spacial and color information, we can use this function as follows:
% r = i(:,:,1);
% g = i(:,:,2);
% b = i(:,:,3);
% [c r] = meshgrid(1:size(i,1), 1:size(i,2));
% data_vecs = [r(:) g(:) b(:) r(:) c(:)];
% [ data_idxs centroids ] = kmeansK( data_vecs, k );
% d = reshape(data_idxs, size(i,1), size(i,2));
% imagesc(d);
Zitieren als
Ahmad (2024). KMeans Segmentation - MEX (https://www.mathworks.com/matlabcentral/fileexchange/27969-kmeans-segmentation-mex), MATLAB Central File Exchange. Abgerufen .
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Cluster Analysis and Anomaly Detection >
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Inspiriert: Sparsified K-Means
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Version | Veröffentlicht | Versionshinweise | |
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1.2.0.0 | Comments update. Plus shifted some functionality to a utils file |
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1.1.0.0 | Included some input checking! |
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1.0.0.0 |