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Discriminatively Trained Latent Ordinal Model for Video Classification.

Authors
  • Sikka, Karan
  • Sharma, Gaurav
Type
Published Article
Journal
IEEE transactions on pattern analysis and machine intelligence
Publication Date
Aug 22, 2017
Identifiers
DOI: 10.1109/TPAMI.2017.2741482
PMID: 28841549
Source
Medline
License
Unknown

Abstract

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. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.

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