Learning Hierarchical Similarity Metrics

  • Dhruv Mahajan | Microsoft Research India

Categories in multi-class data are often part of an underlying semantic taxonomy. Recent work in object classification has found interesting ways to use this taxonomy structure to develop better recognition algorithms. Here we propose a novel framework to learn similarity metrics using the class taxonomy. We show that a nearest neighbor classifier using the learned metrics gets improved performance over the best discriminative methods. Moreover, by incorporating the taxonomy, our learned metrics can also help in some taxonomy specific applications. We show that the metrics can help determine the correct placement of a new category that was not part of the original taxonomy, and can provide effective classification amongst categories local to specific sub-trees of the taxonomy.

Speaker Details

Dhruv Mahajan is an Applied Scientist in the Applied Sciences team at Microsoft Research India since July 2012. Previously, he was a Research Scientist at Yahoo Research Bangalore from 2009. He completed his Ph.D. from the Computer Graphics Group at Columbia University in 2009 under Prof. Ravi Ramamoorthi and Prof. Peter Belhumeur, on the theoretical analysis of light transport. He did his Masters in CS from Columbia University in 2006 and undergrad. in Computer Science from Indian Institute Of Technology Delhi (IIT-Delhi) in 2004. His research interests include Computer Vision, Machine Learning and Computer Graphics.

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