32,661 research outputs found
Model-based segmentation and classification of trajectories (Extended abstract)
We present efficient algorithms for segmenting and classifying a trajectory based on a parameterized movement model like the Brownian bridge movement model. Segmentation is the problem of subdividing a trajectory into parts such that each art is homogeneous in its movement characteristics. We formalize this using the likelihood of the model parameter. We consider the case where a discrete set of m parameter values is given and present an algorithm to compute an optimal segmentation with respect to an information criterion in O(nm) time for a trajectory with n sampling points. Classification is the problem of assigning trajectories to classes. We present an algorithm for discrete classification given a set of trajectories. Our algorithm computes the optimal classification with respect to an information criterion in O(m^2 + mk(log m + log k)) time for m parameter values and k trajectories, assuming bitonic likelihood functions
Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)
Diffusion processes are important in several physical, chemical, biological
and human phenomena. Examples include molecular encounters in reactions,
cellular signalling, the foraging of animals, the spread of diseases, as well
as trends in financial markets and climate records. Deviations from Brownian
diffusion, known as anomalous diffusion, can often be observed in these
processes, when the growth of the mean square displacement in time is not
linear. An ever-increasing number of methods has thus appeared to characterize
anomalous diffusion trajectories based on classical statistics or machine
learning approaches. Yet, characterization of anomalous diffusion remains
challenging to date as testified by the launch of the Anomalous Diffusion
(AnDi) Challenge in March 2020 to assess and compare new and pre-existing
methods on three different aspects of the problem: the inference of the
anomalous diffusion exponent, the classification of the diffusion model, and
the segmentation of trajectories. Here, we introduce a novel method (CONDOR)
which combines feature engineering based on classical statistics with
supervised deep learning to efficiently identify the underlying anomalous
diffusion model with high accuracy and infer its exponent with a small mean
absolute error in single 1D, 2D and 3D trajectories corrupted by localization
noise. Finally, we extend our method to the segmentation of trajectories where
the diffusion model and/or its anomalous exponent vary in time
Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)
Diffusion processes are important in several physical, chemical, biological and human phenomena. Examples include molecular encounters in reactions, cellular signalling, the foraging of animals, the spread of diseases, as well as trends in financial markets and climate records. Deviations from Brownian diffusion, known as anomalous diffusion (AnDi), can often be observed in these processes, when the growth of the mean square displacement in time is not linear. An ever-increasing number of methods has thus appeared to characterize anomalous diffusion trajectories based on classical statistics or machine learning approaches. Yet, characterization of anomalous diffusion remains challenging to date as testified by the launch of the AnDi challenge in March 2020 to assess and compare new and pre-existing methods on three different aspects of the problem: the inference of the anomalous diffusion exponent, the classification of the diffusion model, and the segmentation of trajectories. Here, we introduce a novel method (CONDOR) which combines feature engineering based on classical statistics with supervised deep learning to efficiently identify the underlying anomalous diffusion model with high accuracy and infer its exponent with a small mean absolute error in single 1D, 2D and 3D trajectories corrupted by localization noise. Finally, we extend our method to the segmentation of trajectories where the diffusion model and/or its anomalous exponent vary in time
A Study of Actor and Action Semantic Retention in Video Supervoxel Segmentation
Existing methods in the semantic computer vision community seem unable to
deal with the explosion and richness of modern, open-source and social video
content. Although sophisticated methods such as object detection or
bag-of-words models have been well studied, they typically operate on low level
features and ultimately suffer from either scalability issues or a lack of
semantic meaning. On the other hand, video supervoxel segmentation has recently
been established and applied to large scale data processing, which potentially
serves as an intermediate representation to high level video semantic
extraction. The supervoxels are rich decompositions of the video content: they
capture object shape and motion well. However, it is not yet known if the
supervoxel segmentation retains the semantics of the underlying video content.
In this paper, we conduct a systematic study of how well the actor and action
semantics are retained in video supervoxel segmentation. Our study has human
observers watching supervoxel segmentation videos and trying to discriminate
both actor (human or animal) and action (one of eight everyday actions). We
gather and analyze a large set of 640 human perceptions over 96 videos in 3
different supervoxel scales. Furthermore, we conduct machine recognition
experiments on a feature defined on supervoxel segmentation, called supervoxel
shape context, which is inspired by the higher order processes in human
perception. Our ultimate findings suggest that a significant amount of
semantics have been well retained in the video supervoxel segmentation and can
be used for further video analysis.Comment: This article is in review at the International Journal of Semantic
Computin
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