51,660 research outputs found
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
Viewpoint Discovery and Understanding in Social Networks
The Web has evolved to a dominant platform where everyone has the opportunity
to express their opinions, to interact with other users, and to debate on
emerging events happening around the world. On the one hand, this has enabled
the presence of different viewpoints and opinions about a - usually
controversial - topic (like Brexit), but at the same time, it has led to
phenomena like media bias, echo chambers and filter bubbles, where users are
exposed to only one point of view on the same topic. Therefore, there is the
need for methods that are able to detect and explain the different viewpoints.
In this paper, we propose a graph partitioning method that exploits social
interactions to enable the discovery of different communities (representing
different viewpoints) discussing about a controversial topic in a social
network like Twitter. To explain the discovered viewpoints, we describe a
method, called Iterative Rank Difference (IRD), which allows detecting
descriptive terms that characterize the different viewpoints as well as
understanding how a specific term is related to a viewpoint (by detecting other
related descriptive terms). The results of an experimental evaluation showed
that our approach outperforms state-of-the-art methods on viewpoint discovery,
while a qualitative analysis of the proposed IRD method on three different
controversial topics showed that IRD provides comprehensive and deep
representations of the different viewpoints
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