299,607 research outputs found
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
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
On The Effect of Hyperedge Weights On Hypergraph Learning
Hypergraph is a powerful representation in several computer vision, machine
learning and pattern recognition problems. In the last decade, many researchers
have been keen to develop different hypergraph models. In contrast, no much
attention has been paid to the design of hyperedge weights. However, many
studies on pairwise graphs show that the choice of edge weight can
significantly influence the performances of such graph algorithms. We argue
that this also applies to hypegraphs. In this paper, we empirically discuss the
influence of hyperedge weight on hypegraph learning via proposing three novel
hyperedge weights from the perspectives of geometry, multivariate statistical
analysis and linear regression. Extensive experiments on ORL, COIL20, JAFFE,
Sheffield, Scene15 and Caltech256 databases verify our hypothesis. Similar to
graph learning, several representative hyperedge weighting schemes can be
concluded by our experimental studies. Moreover, the experiments also
demonstrate that the combinations of such weighting schemes and conventional
hypergraph models can get very promising classification and clustering
performances in comparison with some recent state-of-the-art algorithms
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