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Sequential Topic Models for Mining Recurrent Activities and their Relationships

Publications associées (69)

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What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data

Daniel Gatica-Perez, Katayoun Farrahi

We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. ...

IDIAP2008

Analyzing Flickr Groups

Daniel Gatica-Perez, Radu Andrei Negoescu

There is an explosion of community-generated multimedia content available online. In particular, Flickr constitutes a 200-million photo sharing system where users participate following a variety of social motivations and themes. Flickr groups are increasin ...

ACM2008

Analyzing Flickr Groups

Daniel Gatica-Perez, Radu Andrei Negoescu

IDIAP2008

Discovering Human Routines from Cell Phone Data with Topic Models

Daniel Gatica-Perez, Katayoun Farrahi

We present a framework to automatically discover people's routines from information extracted by cell phones. The framework is built from a probabilistic topic model learned on novel bag type representations of activity-related cues (location, proximity an ...

2008

Discovering Human Routines from Cell Phone Data with Topic Models

Daniel Gatica-Perez, Katayoun Farrahi

IDIAP2008

Multi-party Focus of Attention Recognition in Meetings from Head Pose and Multimodal Contextual Cues

Jean-Marc Odobez, Silèye Oumar Ba

We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants from their head pose and contextual cues. The main contribution of the paper is the use of a head pose posterior distribution as a representation of the head ...

IDIAP2007

Dynamical Dirichlet Mixture Model

Jean-Marc Odobez, Le Chen

In this report, we propose a statistical model to deal with the discrete-distribution data varying over time. The proposed model -- HMM+DM -- extends the Dirichlet mixture model to the dynamic case: Hidden Markov Model with Dirichlet mixture output. Both t ...

IDIAP2007

Statistical inference for olfactometer data

Anthony Christopher Davison, Ingrid Ricard

Olfactometer experiments are used to determine the effect of odours on the behaviour of organisms such as insects or nematodes, and typically result in data comprising many groups of small overdispersed counts. We develop a non-homogeneous Markov chain mod ...

2007

Infinite Models for Speaker Clustering

Fabio Valente

In this paper we propose the use of infinite models for the clustering of speakers. Speaker segmentation is obtained trough a Dirichlet Process Mixture (DPM) model which can be interpreted as a flexible model with an infinite a priori number of components. ...

2006

Infinite Models for Speaker Clustering

Fabio Valente

IDIAP2006