[Colloquium] TTIC Talks: Dahua Lin, MIT

[Liv Leader]

When:     Wednesday, March 14 @ 11 a.m.

Where:    TTIC, 6045 S. Kenwood Avenue, Room 526

Who:       Dahua Lin, MIT

Title:       Visual Modeling with Bayesian Nonparametrics

Modeling and understanding complex visual scenes is one of the
fundamental goals of
computer vision. My thesis research aims to develop a generative model
of dynamic scenes
that integrates different aspects, notably appearance, motion, and
layering. This model not
only provides coherent interpretation of the scenes by bringing
different views together, but
also leads to effective algorithms for solving practical tasks. In this
talk, I will describe the
overall framework of the model and then focus on a specific component,
namely the model
of image appearance. In particular, this appearance model incorporates
a patch manifold
with a conditional Markov random field in a probabilistically
consistent way, achieving great
capability of expressing rich details while maintaining global coherence.
Furthermore, the appearance model, which considers each video frame as
a composite
of local patches, provides a useful representation for semantic
analysis. A mixture of visual
topics is then built thereon to capture the semantic aspect. The need
to evolve the topic
mixture model over time motivates an important work in my thesis
research, namely the
construction of dependent Dirichlet processes (DDPs). Dirichlet
processes are widely used
in nonparametric Bayesian models to provide priors over mixture
components, and how to
incorporate temporal dependency between DPs has been an open problem.
To address this,
I develop a new construction of DDPs that exploit the connections
between Poisson and
Dirichlet processes, enabling the use of various forms of dependency
between nonparametric
mixture models.
Both the generative model of visual scenes and the new DDP
construction have been
tested on a variety of applications. I will present the results on
image/video denoising and
dynamic topic analysis (on both videos and documents). The models and
techniques to be
presented constitute an initial but significant step towards the goal
of modeling complex
phenomena. I will also discuss several directions that I wish to
follow to advance this work.

Host: Greg Shakhnarovich, greg at ttic.edu

-- 
Liv Leader
Human Resources Coordinator

Toyota Technological Institute Chicago
6045 S Kenwood Ave
Chicago, IL 60637
Phone- (773) 702-5033
Fax-     (773) 834-9881
Email-  lleader at ttic.edu
Web-   www.ttic.edu