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...
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...
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ...
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...
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 ...
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...
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...
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...
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...