Spatio-temporal Event Classification using Time-series Kernel based Structured Sparsity.

ABSTRACT: In many behavioral domains, such as facial expression and gesture, sparse structure is prevalent. This sparsity would be well suited for event detection but for one problem. Features typically are confounded by alignment error in space and time. As a consequence, high-dimensional representations such as SIFT and Gabor features have been favored despite their much greater computational cost and potential loss of information. We propose a Kernel Structured Sparsity (KSS) method that can handle both the temporal alignment problem and the structured sparse reconstruction within a common framework, and it can rely on simple features. We characterize spatio-temporal events as time-series of motion patterns and by utilizing time-series kernels we apply standard structured-sparse coding techniques

SUBMITTER: Jeni LA 

PROVIDER: S-EPMC5098425 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature