[Colloquium] Sameer Shirdhonkar: University of Maryland, College Park- TTI-C Talk

[Julia MacGlashan]

When:             Wednesday, April 30 @ 10:00am

 

Where:            TTI-C Conference Room, 1427 E. 60th St.

 

Who:                Sameer Shirdhonkar, University of Maryland, College Park

 

Topic:              Linear time approximation to the earth mover's distance
for comparing histograms

 

 

The earth mover's distance (EMD) or Vasershtein metric is a perceptually
meaningful metric used for comparing histograms in various computer vision
applications such as content based image retrieval.  Computing the EMD
involves solving a Kantorovich-Rubinstein (KR) transshipment problem and
requires about cubic time. I  will present a fast linear time approximation
method for solving this class of KR transshipment problems. The
approximation, called wavelet EMD (WEMD), is achieved by transforming the
dual problem into the wavelet domain, where it admits an explicit solution.
It is a weighted norm on the wavelet coefficients of the difference
histograms and is equivalent to the EMD, i.e.  the ratio of the two has
lower and upper bounds.

I will use colour histogram based image retrieval experiments to show that
wavelet EMD is a good approximation to EMD with similar performance, but
requires much less computation time.

 

Briefly, I will talk about my previous work on the importance of
non-negative lighting in model based recognition of specular objects. I will
describe how to constrain the time domain values of a function when you can
only manipulate its frequency domain coefficients.

 

I will also demonstrate the Columbia-Maryland-Smithsonian electronic field
guide for the identification of plant species using leaf images.

 

This is joint work with my advisor Prof. David Jacobs. The electronic field
guide is joint work with many people at the Columbia University, the
University of Maryland and the Smithsonian Institution.

 

 

Contact:          Greg Shakhnarovich, TTI-C         greg at tti-c.org
834-2572

 

 

 

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