future.graph.merge_hierarchical

bricklemacho at gmail.com bricklemacho at gmail.com
Mon Aug 31 10:40:28 EDT 2015


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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