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Robustness, replicability and scalability in topic modelling

Related publications (32)

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Modeling semantic aspects for cross-media image indexing

Daniel Gatica-Perez, Florent Monay Michaud

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content ...

2007

Finding groups of people in Google news

Daniel Gatica-Perez

In this paper, we study the problem of content-based social network discovery among people who frequently appear in world news. Google news is used as the source of data. We describe a probabilistic framework for associating people with groups. A low-dimen ...

2006

A Thousand Words in a Scene

Daniel Gatica-Perez, Jean-Marc Odobez, Pedro Manuel Da Silva Quelhas

This paper presents a novel approach for visual scene modeling and classification, investigating the combined use of text modeling methods and local invariant features. Our work attempts to elucidate (1) whether a text-like \emph{bag-of-visterms} represent ...

IDIAP2005

Modeling semantic aspects for cross-media image indexing

Daniel Gatica-Perez, Florent Monay Michaud

IDIAP2005

Modeling Scenes with Local Descriptors and Latent Aspects

Daniel Gatica-Perez, Jean-Marc Odobez, Florent Monay Michaud, Pedro Manuel Da Silva Quelhas

We present a new approach to model visual scenes in image collections, based on local invariant features and probabilistic latent space models. Our formulation provides answers to three open questions:(1) whether the invariant local features are suitable f ...

2005

Thematic Annotation: extracting concepts out of documents

Martin Rajman

Semantic document annotation may be useful for many tasks. In particular, in the framework of the MDM project(http://www.issco.unige.ch/projects/im2/mdm/), topical annotation -- i.e. the annotation of document segments with tags identifying the topics disc ...

2004

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IDIAP2004

We address the problem of unsupervised image auto-annotation with probabilistic latent space models. Unlike most previous works, which build latent space representations assuming equal relevance for the text and visual modalities, we propose a new way of m ...

2004

IDIAP2004

On Image Auto-Annotation with Latent Space Models

Daniel Gatica-Perez, Florent Monay Michaud

Image auto-annotation, i.e., the association of words to whole images, has attracted considerable attention. In particular, unsupervised, probabilistic latent variable models of text and image features have shown encouraging results, but their performance ...

2003