Re: Graph Cuts implementation

Thanks Ray, That was a great read! Slimcuts look promising. Do you have any thoughts on the implementation?

From what I gather each node must be visited and then to check if any adjacent edge has a capacity larger than the sum of the capacities of the remaining adjacent edges. This process is seemingly them performed repeatedly. This will break the regular structure of the graph meaning that vertex indicies will have to be looked up and I can't figure out how they manage to attain the reported performance. I guess once they have the reduced graph graph-cuts is fast, but I can't see how creating the reducing graph can be fast.

They did however mention that C source files are available for research purposes - Does GSOC / scikit-image count as research? Marc On Tuesday, June 25, 2013 3:22:52 AM UTC+2, Thouis (Ray) Jones wrote:

I would suggest implementing some sort of graph simplification, as in: http://www.3dtv-con2009.org/papers/data/883/paper_EMMCVPR_11.pdf or http://hal.archives-ouvertes.fr/docs/00/78/66/55/PDF/main.pdf

These can be quite effective at reducing the complexity of the graph for planar st-mincut problems, without being too difficult to implement.

Ray Jones

On Sun, Jun 23, 2013 at 6:31 PM, Marc de Klerk javascript:

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wrote:

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Hi Emmanuelle,

I'll have to go have a look weather it's that simple, but thanks for the pointing me in the direction of joblib... In the mean I got a lot of the scaffolding down and put together a demo for Grow Cuts on the GPU. Installation instructions are on my gsoc blog - http://mygsoc.blogspot.com/2013/06/a-kickstart-to-first-week.html

Cheers! Marc

On Sunday, June 23, 2013 11:44:03 PM UTC+2, Emmanuelle Gouillart wrote:

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Hi Mark,

thanks for this first survey. I'll read articles [2] and [4] in the next days, so that I can give you more feedback. For the multicore programming, is it an embarrassingly parallel problem where you could use joblib.Parallel (or simply multiprocessing), or do you have a large number of low-level "small operations" that need to be performed together? Anyway, maybe you can write a first version of the code that is not parallel, convince yourself that the implementation is correct, and only after think about the parallelization?

Cheers, Emmanuelle

On Thu, Jun 20, 2013 at 03:17:16AM -0700, Marc de Klerk wrote:

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Hi Everyone,

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One of items on my todo list in first three weeks of my GSOC is to implement a CPU Graph-cuts. I've made a couple points from a preliminary literature survey and would like to get some feedback / recommendations / advice.

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Graph cut algorithms can generally be regarded as either begin push-relabel (PR) or augmenting paths (AP) style. - The goto algorithm has typically been the Boykov and Kolmogorov algorithm (AP) [1] - The current state-of-art graph-cut is packaged as GridCut, product of [2] - Based on [1] - Multi core (from [3]) - Cache efficient memory layout (from [2]) - For grid-like topologies - A push relabel variant exists from [4] - Multi core (from [3]) - Not bound by memory constraints - For grid-like topologies - From what I can gather BK typically outperforms other approaches such as the push-relabel algorithm, but to what extent I'm can't tell from the literature… - [4] describes memory layout strategies to optimize caching for both AP and PR style algorithms, but that choose the implement their strategies on BK. - PR style algorithms are not limited by memory constraints, whereas AP style algorithms are, however doing graph cuts on a coarse to fine scale seem to be an answer. - PR is the only feasible way to do graph cuts on the GPU.

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So I'm suggestions to implement the PR style algorithm of [4] using the strategies described in [2] to speed things up. This then let's us make a fairer comparison between CPU and GPU versions of the graph cut

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Does this sound like a good way forward? It does require some multi-core programming which I'm not familiar with - are there any examples of something similar in scikit-image?

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[1] Boykov, Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/ max- flow algorithms for energy minimization in vision. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(9), 1124–1137. doi:10.1109/ TPAMI.2004.60 [2] Jamriska, O., Sykora, D., & Hornung, A. (2012). Cache-efficient graph cuts on structured grids (pp. 3673–3680). Presented at the Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. doi:10.1109/ CVPR.2012.6248113 [3] Liu, J., & Sun, J. (2010). Parallel graph-cuts by adaptive bottom-up merging (pp. 2181–2188). Presented at the Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. doi:10.1109/CVPR.2010.5539898 [4] Delong, A., & Boykov, Y. (2008). A Scalable graph-cut algorithm for N-D grids (pp. 1–8). Presented at the Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. doi:10.1109/CVPR.2008.4587464

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