Abstract

The ability to robustly cluster faces in movies is a necessary step in understanding media content representations of people along dimensions such as gender and age. Building upon the successes of sparse subspace clustering (SSC) in uncovering the underlying structure of the data, in this paper we propose an algorithm called Constraint Propagation Sparse Subspace Clustering (CP-SSC) for applications such as face clustering in videos where pairwise sample constraints (must-link and cannot-link sample pairs) are available in the processing pipeline since detected faces can be tracked locally in time. We learn the subspace structure while simultaneously incorporating the pairwise constraints to construct a similarity matrix needed for clustering. Our joint formulation uses low-rank matrix completion to propagate the initial pairwise constraints, that are used to reinforce the subspace representation during …

Date

2019

Authors

Krishna Somandepalli, Shrikanth Narayanan

Conference

ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

4065-4069

Publisher

IEEE