Discriminatively Trained Latent Ordinal Model for Video Classification

Understand

We study the problem of video classification for facial analysis and human action recognition.

• We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg.
• onset and offset phase for "smile", running and jumping for "highjump").
• The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately.