Joint design of data analysis algorithms and user
interface for video applications
Nebojsa Jojic, Microsoft
Research
Sumit Basu, Microsoft
Research
Nemanja Petrovic,
Brendan Frey,
Thomas Huang,
The graphical modeling paradigm provides a way of
representing data through hidden causes of variability which can be estimated
from the data in an unsupervised manner. Recently, a lot of research has been
dedicated to finding efficient inference and learning engines for graphical
models in general, as well as to finding various ways of using graphical models
to perform recognition, classification, segmentation, and tracking tasks in
video applications. Little research, however, has focused on another advantage
of a graphical model - by discovering the structural elements in the data, it
renders the data much easier to browse, manipulate, or interact with. In this
paper, we present several ideas on how the user interface and the data analysis
tools can be designed jointly starting from an appropriate data representation
scheme and a generative model based on it. We base our approach on three basic
principles:
• Compatibility of the graphical model’s structure
with our own perception of the world
• Simplicity in representation, leading to more
efficient inference
• Providing intuitive interactivity on the level of
hidden causes of variability
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