Metric learning for semi-supervised clustering of region covariance descriptors

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

in this paper we extend distance metric learning to a new class of descriptors known as Region Covariance Descriptors. Region covariances are becoming increasingly popular as features for object detection and classification over the past few years. Given a set of pairwise constraints by the user, we want to perform semi-supervised clustering of these descriptors aided by metric learning approaches. The covariance descriptors belong to the special class of symmetric positive definite (SPD) tensors, and current algorithms cannot deal with them directly without violating their positive definiteness. In our framework, the distance metric on the manifold of SPD matrices is represented as an L2 distance in a vector space, and a Mahalanobis-type distance metric is learnt in the new space, in order to improve the performance of semi-supervised clustering of region covariances. We present results from clustering of covariance descriptors representing different human images, from single and multiple camera views. This transformation from a set of positive definite tensors to a Euclidean space paves the way for the application of many other vector-space methods to this class of descriptors.

Original languageEnglish (US)
Title of host publication2009 3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009
DOIs
StatePublished - Dec 23 2009
Event2009 3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009 - Como, Italy
Duration: Aug 30 2009Sep 2 2009

Publication series

Name2009 3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009

Other

Other2009 3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009
Country/TerritoryItaly
CityComo
Period8/30/099/2/09

Keywords

  • Appearance clustering
  • Distance metric learning
  • Region covariance descriptors
  • Semi-supervised clustering

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