108,261 research outputs found
ClustGeo: an R package for hierarchical clustering with spatial constraints
In this paper, we propose a Ward-like hierarchical clustering algorithm
including spatial/geographical constraints. Two dissimilarity matrices
and are inputted, along with a mixing parameter . The
dissimilarities can be non-Euclidean and the weights of the observations can be
non-uniform. The first matrix gives the dissimilarities in the "feature space"
and the second matrix gives the dissimilarities in the "constraint space". The
criterion minimized at each stage is a convex combination of the homogeneity
criterion calculated with and the homogeneity criterion calculated with
. The idea is then to determine a value of which increases the
spatial contiguity without deteriorating too much the quality of the solution
based on the variables of interest i.e. those of the feature space. This
procedure is illustrated on a real dataset using the R package ClustGeo
Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations
This paper presents a co-clustering technique that, given a collection of
images and their hierarchies, clusters nodes from these hierarchies to obtain a
coherent multiresolution representation of the image collection. We formalize
the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a
linear programming relaxation approach that makes effective use of information
from hierarchies. Initially, we address the problem of generating an optimal,
coherent partition per image and, afterwards, we extend this method to a
multiresolution framework. Finally, we particularize this framework to an
iterative multiresolution video segmentation algorithm in sequences with small
variations. We evaluate the algorithm on the Video Occlusion/Object Boundary
Detection Dataset, showing that it produces state-of-the-art results in these
scenarios.Comment: International Conference on Computer Vision (ICCV) 201
Planar Ultrametric Rounding for Image Segmentation
We study the problem of hierarchical clustering on planar graphs. We
formulate this in terms of an LP relaxation of ultrametric rounding. To solve
this LP efficiently we introduce a dual cutting plane scheme that uses minimum
cost perfect matching as a subroutine in order to efficiently explore the space
of planar partitions. We apply our algorithm to the problem of hierarchical
image segmentation
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