Hey Brickle,

This is a very cool application! Feel free to ask for more guidance about this. The RAG is in future because we weren't sure whether the API would provide enough for all use cases, so if it needs extending, this is exactly the kind of feedback we're looking for!

If you've made any interesting changes to the code, but want help, you can submit them as a pull request on GitHub. Just start the PR title with "[WIP]" for "work in progress".

Thanks!

Juan.




On Wed, Sep 9, 2015 at 11:23 PM, bricklemacho@gmail.com <bricklemacho@gmail.com> wrote:

Hi,

Been away for a few days.  Thanks for that, clears a few things up.  I am looking at modifying the RAG code from the furture.graph.

Regards,

Brickle.


On 3/09/2015 11:26 pm, Vighnesh Birodkar wrote:
Hello

At each step, the edge with the least weight is merged. The code uses a min heap for that. You could take an inverse of your measure such that similar nodes have lesser values. 'in_place' just decides whether a new node is created for a merge or not, it most likely won't do what you need in this case.

I hope I was clear.

Thanks
Vighnesh

On Tuesday, September 1, 2015 at 7:24:31 PM UTC-4, bricklemacho wrote:
Hi All,

I am looking at generating some detection proposals, see  Hosang, Jan, et al. "What makes for effective detection proposals?." arXiv preprint arXiv:1502.05082 (2015), http://arxiv.org/pdf/1502.05082.pdf    Starting with the Selective Search algorithm, Section 3 of  Uijlings, Jasper RR, et al. "Selective search for object recognition." International journal of computer vision 104.2 (2013): 154-171,    https://staff.fnwi.uva.nl/th.gevers/pub/GeversIJCV2013.pdf

The basic idea is the performing a hierarchical merging of the image, where each new merge get added to the list of regions suspected to contain an object, you can capture objects at all scales.  This reduces the search space significantly than say compared to floating window.  The output is NOT a image segmentaiton, rather a list of regions (bounding boxes) of potential objects (deteciton proposals).

I have looked in the gallery at RAG Merging http://scikit-image.org/docs/dev/auto_examples/plot_rag_merge.html,  fairly confident I can setup the callback methods to provided the similarity measure.   I am naively hoping that future.graph.hierarchical(), even though it seems to output a segmentation (labels), can be easily adapted to the task.    What would be the best way to have future.graph.merge_hierarchica() merge regions with the "highest" similarity measure, rather thana threshold?   What would be the best way future.graph.merge_hierarchica() save each merged region?   Tried setting  "in_place" to false, but didn't notice any difference.

Any help appreciated,

Brickle.
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