Active Co-Analysis of a Set of Shapes

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                Joint Seminars
                     by
Dr. Yunhai WANG and Dr. Liangliang NAN

Date:   Monday, 24 September 2012

Venue:  Lecture Theatre F (near lifts 25/26), HKUST

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Speaker:        Dr. Yunhai WANG
                Shenzhen Institutes of Advanced Technology
                Shenzhen, China

Title:          "Active Co-Analysis of a Set of Shapes"

Time:           4:00pm - 4:30pm

Abstract:

Unsupervised co-analysis of a set of shapes is a difficult problem since
the geometry of the shapes alone cannot always fully describe the
semantics of the shape parts. In this paper, we propose a semi-supervised
learning method where the user actively assists in the co-analysis by
iteratively providing inputs that progressively constrain the system. We
introduce a novel constrained clustering method based on a spring system
which embeds elements to better respect their inter-distances in feature
space together with the user given set of constraints. We also present an
active learning method that suggests to the user where his input is likely
to be the most effective in refining the results. We show that each single
pair of constraints affects many relations across the set. Thus, the
method requires only a sparse set of constraints to quickly converge
toward a consistent and error-free semantic labeling of the set.


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Biography:

Dr. Yunhai Wang is an assistant Professor in Shenzhen Institutes of
Advanced Technology, Shenzhen, China. He received his Ph.D. degree from
Supercomputing Center, Chinese Academy of Sciences in 2011. His research
deals with the development of visualization and computer graphics
techniques that help people see and understand the data. He is
particularly interested in developing machine learning algorithms for data
visualization and shape analysis.