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This wrapper for Matlab was written by Shai Bagon ([email protected]). Department of Computer Science and Applied Mathmatics Wiezmann Institute of Science http://www.wisdom.weizmann.ac.il/

The core cpp application was written by Olga Veksler
(available from http://www.csd.uwo.ca/faculty/olga/code.html):

[1] Efficient Approximate Energy Minimization via Graph Cuts Yuri Boykov, Olga Veksler, Ramin Zabih, IEEE transactions on PAMI, vol. 20, no. 12, p. 1222-1239, November 2001.

[2] What Energy Functions can be Minimized via Graph Cuts? Vladimir Kolmogorov and Ramin Zabih. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 26, no. 2, February 2004, pp. 147-159.

[3] An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. Yuri Boykov and Vladimir Kolmogorov. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 26, no. 9, September 2004, pp. 1124-1137.

[4] Matlab Wrapper for Graph Cut. Shai Bagon. in https://github.com/shaibagon/GCMex, December 2006.

This software can be used only for research purposes, you should cite ALL of the aforementioned papers in any resulting publication. If you wish to use this software (or the algorithms described in the aforementioned paper) for commercial purposes, you should be aware that there is a US patent:

   R. Zabih, Y. Boykov, O. Veksler,
   "System and method for fast approximate energy minimization via
   graph cuts ",
   United Stated Patent 6,744,923, June 1, 2004

The Software is provided "as is", without warranty of any kind.

Run compile_gc to compile all c/c++ sources into mex files.

Performing Graph Cut energy minimization operations on graphs.

Usage: [gch ...] = GraphCut(mode, ...);

Inputs:

• mode: a string specifying mode of operation. See details below.

Output:

• gch: A handle to the constructed graph. Handle this handle with care and don't forget to close it in the end!

Possible modes:

• 'open': Create a new graph object [gch] = GraphCut('open', DataCost, SmoothnessCost); [gch] = GraphCut('open', DataCost, SmoothnessCost, vC, hC); [gch] = GraphCut('open', DataCost, SmoothnessCost, SparseSmoothness);

  Inputs: - DataCost a height by width by num_labels matrix where Dc(r,c,l) equals the cost for assigning label l to pixel at (r,c) Note that the graph dimensions, and the number of labels are deduced form the size of the DataCost matrix. When using SparseSmoothness Dc is of (L)x(P) where L is the number of labels and P is the number of nodes/pixels in the graph. - SmoothnessCost a #labels by #labels matrix where Sc(l1, l2) is the cost of assigning neighboring pixels with label1 and label2. This cost is spatialy invariant - vC, hC:optional arrays defining spatialy varying smoothness cost. Single precission arrays of size width*height. The smoothness cost is computed using: V_pq(l1, l2) = V(l1, l2) * w_pq where V is the SmoothnessCost matrix w_pq is spatialy varying parameter: if p=(r,c) and q=(r+1,c) then w_pq = vCue(r,c) if p=(r,c) and q=(r,c+1) then w_pq = hCue(r,c) (therefore in practice the last column of vC and the last row of vC are not used). - SparseSmoothness: a sparse matrix defining both the graph structure (might be other than grid) and the spatialy varying smoothness term. Must be real positive sparse matrix of size num_pixels by num_pixels, each non zero entry (i,j) defines a link between pixels i and j with w_pq = SparseSmoothness(i,j).

• 'set': Set labels [gch] = GraphCut('set', gch, labels)

  Inputs: - labels: a width by height array containing a label per pixel. Array should be the same size of the grid with values [0..num_labels].

• 'get': Get current labeling [gch labels] = GraphCut('get', gch)

  Outputs: - labels: a height by width array, containing a label per pixel. note that labels values are in range [0..num_labels-1].

• 'energy': Get current values of energy terms [gch se de] = GraphCut('energy', gch) [gch e] = GraphCut('energy', gch)

  Outputs: - se: Smoothness energy term. - de: Data energy term. - e = se + de

• 'expand': Perform labels expansion [gch labels] = GraphCut('expand', gch) [gch labels] = GraphCut('expand', gch, iter) [gch labels] = GraphCut('expand', gch, [], label) [gch labels] = GraphCut('expand', gch, [], label, indices)

  When no inputs are provided, GraphCut performs expansion steps until it converges.

  Inputs: - iter: a double scalar, the maximum number of expand iterations to perform. - label: scalar denoting the label for which to perfom expand step (labels are [0..num_labels-1]). - indices: array of linear indices of pixels for which expand step is computed.

  Outputs: - labels: a width*height array of type int32, containing a label per pixel. note that labels values must be is range [0..num_labels-1].

• 'swap': Perform alpha - beta swappings [gch labels] = GraphCut('swap', gch) [gch labels] = GraphCut('swap', gch, iter) [gch labels] = GraphCut('swap', gch, label1, label2)

  When no inputs are provided, GraphCut performs alpha - beta swaps steps until it converges.

  Inputs: - iter: a double scalar, the maximum number of swap iterations to perform. - label1, label2: int32 scalars denoting two labels for swap step.

  Outputs: - labels: a width*height array of type int32, containing a label per pixel. note that labels values must be is range [0..num_labels-1].

• 'truncate': truncating (or not) violating expansion terms (see Rother etal. Digital Tapestry, CVPR2005) [gch truncate_flag] = GraphCut('truncate', gch, trancate_flag);

  When no truncate_flag is provided the function returns the current state of truncation

  Inputs: - trancate_flag: set truncate_flag to this state

  Outputs: - trancate_flag: current state (after modification if applicable)

• 'close': Close the graph and release allocated resources. [gch] = GraphCut('close', gch);

You can use sparse_adj_matrix function to create sparse adjacency matrix for grid graphs.

See doc sparse_adj_matrix for more information.