6180007mean_shift.rar_mean shift_speakxac_super pixel_超像素分割

function [mode] = mean_shift(data, h, epsilon, dist2) % locating the mode of data (local maxima of density function) % using mean shift. % h - Gaussian kernel with parameter h (window size) % eps - epsilon accuracy of mean shift step. i.e. how small m_hG to be % considered as no improvement - NOT RECCOMAND eps = 0!! % data - a dxN matrix of N vector of d dimension % dist2 - pointer ro function that measures distance square % the distance function should be able to take two d by N matrices % and return a vector of length N, where Ni is the distance^2 % between the two i-th vectors. % % % Copyright (c) Bagon Shai % Department of Computer Science and Applied Mathmatics % Wiezmann Institute of Science % http://www.wisdom.weizmann.ac.il/ % % Permission is hereby granted, free of charge, to any person obtaining a copy % of this software and associated documentation files (the "Software"), to deal % in the Software without restriction, subject to the following conditions: % % 1. The above copyright notice and this permission notice shall be included in % all copies or substantial portions of the Software. % 2. No commercial use will be done with this software. % 3. If used in an academic framework - a proper citation must be included. % % The Software is provided "as is", without warranty of any kind. % % Mar. 2007 % NUM_OF_TRAILS = 1; [d N] = size(data); NUM_OF_TRAILS = min(NUM_OF_TRAILS,N); % choose the random starting points rsp = randsample(1:N,NUM_OF_TRAILS); eps2 = epsilon*epsilon; % start from a random x x = data(:, rsp(1)); [mode, score] = mean_shift_iteration(x, data, h, eps2, dist2); for ii = 1:NUM_OF_TRAILS-1 x = data(:,rsp(ii+1)); [m, s] = mean_shift_iteration(x, data, h, eps2, dist2); if ( s > score ) mode = m; score = s; end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [mode, score] = mean_shift_iteration(x, data, h, eps2, dist2, per) % iterate mean shift search from starting point x % compute the first mean shift step [ms, score] = m_hG(x, data, h, dist2); % iterate untill no step is achieved while ( dist2(ms , x) >= eps2 ) x = ms; [ms, score] = m_hG(x, data, h, dist2); end mode = ms; % prune non local maxima points % % if ( ~ pertrube(x, data, h, eps2, dist2) ) % % score = -1; % % end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [LocalMax] = pertrube(x, data, h, eps2, dist2) % we reahed a stationary point, we want to pertrube x a little % and check if its a local maxima or not % the critiria: move x a little if the mean shift returns to the same point % its a local maxima. % pertrubing by random vector of norm h/4 per = rand( size(data,1) ,1)-0.5; per = h * per ./ ( sqrt( dist2(per, zeros(size(per)) ) ) ); [mode, score] = mean_shift_iteration(x + per, data, h, eps2, dist2); % logical variable % the mean shift step must be toward x. LocalMax = ( dist2(mode, x) <= sqrt(eps2) ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [ms, score]=m_hG(x, data, h, dist2) % compute the mean shift of the data % x - relative to x location % data - dxN % h - gaussian window % SUM Xi exp( -.5 || dist(X,Xi)/h ||^2 ) % m = --------------------------------- % h,G SUM exp( -.5 || dist(X,Xi)/h ||^2 ) % e is the argument of the exponent in the term above: i.e. 1xN vector with % ||X-Xi/h||^2 at the i-th column e = dist2( repmat(x, 1, size(data,2)), data ) ./ (h.^2); e = exp(-0.5*e); score = sum(e); % the mean shift ms = (data*e' ./ score);