原始slic算法的matlab示例代码

以下是原始slic算法的matlab示例代码：

function [L, num] = slic(I, K, m)
% I: input image
% K: number of superpixels
% m: compactness parameter

% Convert image to Lab color space
I = double(I);
[L, a, b] = RGB2Lab(I(:,:,1), I(:,:,2), I(:,:,3));

% Initialize cluster centers
[N, M] = size(L);
S = round(sqrt(N*M/K));
cx = S/2:S:N-S/2;
cy = S/2:S:M-S/2;
[X, Y] = meshgrid(cx, cy);
C = [X(:), Y(:)];
nc = size(C, 1);

% Initialize labels and distances
labels = zeros(N, M);
distances = inf(N, M);

% Iterate until convergence
while true
    old_labels = labels;
    old_distances = distances;
    
    % Assign pixels to nearest cluster center
    for i = 1:nc
        x = C(i, 1);
        y = C(i, 2);
        x1 = max(x-S, 1);
        x2 = min(x+S, N);
        y1 = max(y-S, 1);
        y2 = min(y+S, M);
        D = (L(x1:x2, y1:y2)-L(x, y)).^2 + ...
            (a(x1:x2, y1:y2)-a(x, y)).^2 + ...
            (b(x1:x2, y1:y2)-b(x, y)).^2;
        D = D + m*(1/S)^2*(X(x1:x2, y1:y2)-C(i, 1)).^2 + ...
            m*(1/S)^2*(Y(x1:x2, y1:y2)-C(i, 2)).^2;
        idx = D < distances(x1:x2, y1:y2);
        distances(x1:x2, y1:y2) = idx.*D + ~idx.*distances(x1:x2, y1:y2);
        labels(x1:x2, y1:y2) = idx*i + ~idx.*labels(x1:x2, y1:y2);
    end
    
    % Update cluster centers
    for i = 1:nc
        idx = labels == i;
        if any(idx(:))
            C(i, :) = mean([X(idx), Y(idx)]);
            L(i) = mean(L(idx));
            a(i) = mean(a(idx));
            b(i) = mean(b(idx));
        end
    end
    
    % Check for convergence
    if all(labels(:) == old_labels(:)) && all(distances(:) == old_distances(:))
        break;
    end
end

% Output labels and number of superpixels
num = nc;
end

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