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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