future.graph.merge_hierarchical
bricklemacho at gmail.com
bricklemacho at gmail.com
Thu Sep 17 11:11:49 EDT 2015
Hi Juan,
I am working on this in slow time. At the moment I am writing a
generic function to create the RAG. It is similar to rag_mean_colour()
but takes two callbacks, one to describe the nodes and another to
compute the edge weight. Based on Vighnesh advice below, I my use case
I will ensure the "least" weight implies the most similar. Theh first
step will be to have the rag_generic() duplicate rag_mean_colour().
Once I am happy with rag_generic(), I will need to an create alternative
merging process. The alternative method wont use a threshold but rather
at each step will select the "most" similar nodes for merging. This
merge process will terminate once there is a single node in the graph.
The final output will be the initial regions, plus each of the single
merge (hope that make sense).
Anyway, I have created a fork and when basic functionally is working
will submit a PR.
Regards,
Michael.
--
On 16/09/2015 5:34 pm, Juan Nunez-Iglesias wrote:
> 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 at gmail.com
> <bricklemacho at gmail.com <mailto:bricklemacho at 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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