2,348 research outputs found
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation
Nonparametric Bayesian approaches to clustering, information retrieval,
language modeling and object recognition have recently shown great promise as a
new paradigm for unsupervised data analysis. Most contributions have focused on
the Dirichlet process mixture models or extensions thereof for which efficient
Gibbs samplers exist. In this paper we explore Gibbs samplers for infinite
complexity mixture models in the stick breaking representation. The advantage
of this representation is improved modeling flexibility. For instance, one can
design the prior distribution over cluster sizes or couple multiple infinite
mixture models (e.g. over time) at the level of their parameters (i.e. the
dependent Dirichlet process model). However, Gibbs samplers for infinite
mixture models (as recently introduced in the statistics literature) seem to
mix poorly over cluster labels. Among others issues, this can have the adverse
effect that labels for the same cluster in coupled mixture models are mixed up.
We introduce additional moves in these samplers to improve mixing over cluster
labels and to bring clusters into correspondence. An application to modeling of
storm trajectories is used to illustrate these ideas.Comment: Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006
A distributional model of semantic context effects in lexical processinga
One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
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