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Accession Number ADA580102
Title Fast Multiclass Segmentation using Diffuse Interface Methods on Graphs.
Publication Date Feb 2013
Media Count 15p
Personal Author A. Flenner A. G. Percus A. L. Bertozzi C. Garcia-Cardona E. Merkurjev
Abstract We present two graph-based algorithms for multiclass segmentation of high-dimensional data. The algorithms use a diffuse interface model based on the Ginzburg-Landau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs simplex, with the functional's double-well potential modified to handle the multiclass case. The first algorithm minimizes the functional using a convex splitting numerical scheme. The second algorithm is a uses a graph adaptation of the classical numerical Merriman-Bence-Osher (MBO) scheme, which alternates between diffusion and thresholding. We demonstrate the performance of both algorithms experimentally on synthetic data, grayscale and color images and several benchmark data sets such as MNIST, COIL and WebKB. We also make use of fast numerical solvers for finding the eigenvectors and eigenvalues of the graph Laplacian, and take advantage of the sparsity of the matrix. Experiments indicate that the results are competitive with or better than the current state- of-the-art multiclass segmentation algorithms.
Keywords Algorithms
Convex splitting
Diffuse interface
Ginzburg-landau functional
High-dimensional data
Image processing
Mbo scheme

Source Agency Non Paid ADAS
NTIS Subject Category 72F - Statistical Analysis
Corporate Author California Univ., Los Angeles. Dept. of Mathematics.
Document Type Technical report
Title Note N/A
NTIS Issue Number 1325
Contract Number N00014-12-1-0838 N00014-12-1-0040

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