5,150 research outputs found
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian
The extraction of clusters from a dataset which includes multiple clusters
and a significant background component is a non-trivial task of practical
importance. In image analysis this manifests for example in anomaly detection
and target detection. The traditional spectral clustering algorithm, which
relies on the leading eigenvectors to detect clusters, fails in such
cases. In this paper we propose the {\it spectral embedding norm} which sums
the squared values of the first normalized eigenvectors, where can be
significantly larger than . We prove that this quantity can be used to
separate clusters from the background in unbalanced settings, including extreme
cases such as outlier detection. The performance of the algorithm is not
sensitive to the choice of , and we demonstrate its application on synthetic
and real-world remote sensing and neuroimaging datasets
Learning Finer-class Networks for Universal Representations
Many real-world visual recognition use-cases can not directly benefit from
state-of-the-art CNN-based approaches because of the lack of many annotated
data. The usual approach to deal with this is to transfer a representation
pre-learned on a large annotated source-task onto a target-task of interest.
This raises the question of how well the original representation is
"universal", that is to say directly adapted to many different target-tasks. To
improve such universality, the state-of-the-art consists in training networks
on a diversified source problem, that is modified either by adding generic or
specific categories to the initial set of categories. In this vein, we proposed
a method that exploits finer-classes than the most specific ones existing, for
which no annotation is available. We rely on unsupervised learning and a
bottom-up split and merge strategy. We show that our method learns more
universal representations than state-of-the-art, leading to significantly
better results on 10 target-tasks from multiple domains, using several network
architectures, either alone or combined with networks learned at a coarser
semantic level.Comment: British Machine Vision Conference (BMVC) 201
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