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ICWSM
2009
9 years 11 months ago
A Categorical Model for Discovering Latent Structure in Social Annotations
The advent of social tagging systems has enabled a new community-based view of the Web in which objects like images, videos, and Web pages are annotated by thousands of users. Und...
Said Kashoob, James Caverlee, Ying Ding
CLEF
2009
Springer
10 years 2 months ago
Unsupervised Word Decomposition with the Promodes Algorithm
We present Promodes, an algorithm for unsupervised word decomposition, which is based on a probabilistic generative model. The model considers segment boundaries as hidden variable...
Sebastian Spiegler, Bruno Golénia, Peter A....
NIPS
2007
10 years 3 months ago
Learning Visual Attributes
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ...
Vittorio Ferrari, Andrew Zisserman
EMNLP
2007
10 years 3 months ago
Bayesian Document Generative Model with Explicit Multiple Topics
In this paper, we proposed a novel probabilistic generative model to deal with explicit multiple-topic documents: Parametric Dirichlet Mixture Model(PDMM). PDMM is an expansion of...
Issei Sato, Hiroshi Nakagawa
CVPR
2010
IEEE
10 years 9 months ago
Clustering Dynamic Textures with the Hierarchical EM Algorithm
The dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model ...
Antoni Chan, Emanuele Coviello, Gert Lanckriet
KDD
2007
ACM
237views Data Mining» more  KDD 2007»
11 years 2 months ago
Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior
Documents, such as those seen on Wikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and more important to analyze a re...
Issei Sato, Hiroshi Nakagawa
KDD
2008
ACM
257views Data Mining» more  KDD 2008»
11 years 2 months ago
Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model
We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering seman...
Issei Sato, Minoru Yoshida, Hiroshi Nakagawa
ICCV
2007
IEEE
11 years 3 months ago
Non-Parametric Probabilistic Image Segmentation
We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a Mixture of Gaussians) the proposed model is princip...
Marco Andreetto, Lihi Zelnik-Manor, Pietro Perona
CVPR
2008
IEEE
11 years 3 months ago
Simultaneous clustering and tracking unknown number of objects
In this paper, we present a novel on-line probabilistic generative model that simultaneously deals with both the clustering and the tracking of an unknown number of moving objects...
Katsuhiko Ishiguro, Takeshi Yamada, Naonori Ueda
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