101,866 research outputs found
Max-Cut and Max-Bisection are NP-hard on unit disk graphs
We prove that the Max-Cut and Max-Bisection problems are NP-hard on unit disk
graphs. We also show that -precision graphs are planar for >
1 / \sqrt{2}$
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
PersonRank: Detecting Important People in Images
Always, some individuals in images are more important/attractive than others
in some events such as presentation, basketball game or speech. However, it is
challenging to find important people among all individuals in images directly
based on their spatial or appearance information due to the existence of
diverse variations of pose, action, appearance of persons and various changes
of occasions. We overcome this difficulty by constructing a multiple
Hyper-Interaction Graph to treat each individual in an image as a node and
inferring the most active node referring to interactions estimated by various
types of clews. We model pairwise interactions between persons as the edge
message communicated between nodes, resulting in a bidirectional
pairwise-interaction graph. To enrich the personperson interaction estimation,
we further introduce a unidirectional hyper-interaction graph that models the
consensus of interaction between a focal person and any person in a local
region around. Finally, we modify the PageRank algorithm to infer the
activeness of persons on the multiple Hybrid-Interaction Graph (HIG), the union
of the pairwise-interaction and hyperinteraction graphs, and we call our
algorithm the PersonRank. In order to provide publicable datasets for
evaluation, we have contributed a new dataset called Multi-scene Important
People Image Dataset and gathered a NCAA Basketball Image Dataset from sports
game sequences. We have demonstrated that the proposed PersonRank outperforms
related methods clearly and substantially.Comment: 8 pages, conferenc
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Unlike on images, semantic learning on 3D point clouds using a deep network
is challenging due to the naturally unordered data structure. Among existing
works, PointNet has achieved promising results by directly learning on point
sets. However, it does not take full advantage of a point's local neighborhood
that contains fine-grained structural information which turns out to be helpful
towards better semantic learning. In this regard, we present two new operations
to improve PointNet with a more efficient exploitation of local structures. The
first one focuses on local 3D geometric structures. In analogy to a convolution
kernel for images, we define a point-set kernel as a set of learnable 3D points
that jointly respond to a set of neighboring data points according to their
geometric affinities measured by kernel correlation, adapted from a similar
technique for point cloud registration. The second one exploits local
high-dimensional feature structures by recursive feature aggregation on a
nearest-neighbor-graph computed from 3D positions. Experiments show that our
network can efficiently capture local information and robustly achieve better
performances on major datasets. Our code is available at
http://www.merl.com/research/license#KCNetComment: Accepted in CVPR'18. *indicates equal contributio
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