Nov 13, 2012 · 5:30 PM
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Talk title: An introduction to Generalized K-means with image processing applications
In this era of digital Big Data, large scale collections of digital images are proliferating all over the place on and offline. For example
• users on Facebook upload more than 200 million photos every day
• in the medical imaging domain, over 68 million CT scans were performed in the US last year
• IT giants are building enormous visual maps of the world from massive collections of street view images
This explosive growth in image data poses serious challenges in terms of both storage - that is, how do we more efficiently compress rapidly growing collections of images? - and search - that is, how can we more effectively sort through image databases? - and in each case the best solutions developed so far rely heavily on machine learning techniques. Generalized K-means (G-K-means), more commonly called Dictionary Learning, is one of the key machine learning tools researchers such as my self are using to attack the storage problem. While at a high level this technique is really just exactly what you'd expect it to be - a generalized version of the standard K-means where you assign a data point to multiple clusters instead of just one - algorithm-wise it falls into the bucket of modern sparse statistical methods (e.g. compressive sensing, the lasso) which have been mentioned at some previous meetups.
This talk will be a user-friendly introduction to G-K-means with a practical algorithmic and application focus. I'll first review the standard K-means algorithm and its popular application to single image compression. I'll then show how, viewing K-means as a sparse statistical method, you can easily derive the analogous G-K-means model along with a natural greedy algorithm for solving it. Finally I'll show some cool applications of G-K-means to the processing of large databases of images, and discuss its application to the storage problem - that is, to large scale image collection compression.
Jeremy has a masters in math and is a current PhD student in Electrical Engineering at Northwestern. He conducts research in data mining/machine learning, and particularly in sparse statistical models and their applications to large scale image processing and business intelligence. He keeps a blog about research and teaching in the Machine Learning space located at
In his spare time he drinks coffee like there's no tomorrow, teaches self-designed courses in math and engineering to K-12 students through Northwestern University's Center for Talent Development, writes for the public radio program “A Moment of Science”, and takes an occasional improv class at Second City.