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Summary
In the last years, there have been a significant growth of multimedia
information processing applications that employ ideas from statistical
machine learning and probabilistic modeling. In this paradigm,
multimedia data (music, audio, video, images, text, ...) are viewed as
realizations from highly structured stochastic processes. Once a model
is constructed, several interesting problems such as transcription,
coding, classification, restoration, tracking, source separation or
resynthesis etc. can be formulated as Bayesian inference problems. In
this context, graphical models provide a "language" to construct
models for quantification of prior knowledge. Unknown parameters in
this specification are estimated by probabilistic inference. Often,
however, the problem size poses an important challenge and in order to
render the approach feasible, specialized inference methods need to be
tailored to improve the computational speed and efficiency.
The scope of the proposed tutorial is as follows: First, we will
review the fundamentals of probabilistic models, with some focus on
music, video and text data. Then, we will discuss the numerical
techniques for inference in these models. In particular, we will
review exact inference, approximate stochastic inference techniques
such as Markov Chain Monte Carlo, Sequential Monte Carlo and
deterministic (variational) inference techniques. Our ultimate aim is
to provide a basic understanding of probabilistic modeling for
multimedia processing, associated computational techniques and a
roadmap such that information retrieval researchers new to the
Bayesian approach can orient themselves in the relevant literature and
understand the current state of the art.
A longer version of this abstract to be published in the proceedings is here.