future.graph.merge_hierarchical

[bricklemacho at gmail.com]

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
>     <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
>     <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
>     <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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