TITLE:
Learning Actions from the Identity in the Web
AUTHORS:
Khawla Hussein Ali, Tianjiang Wang
KEYWORDS:
Action Recognition, HOG, SVM Classification
JOURNAL NAME:
Journal of Computer and Communications,
Vol.2 No.9,
July
11,
2014
ABSTRACT: This paper proposes an
efficient and simple method for identity recognition in uncontrolled videos.
The idea is to use images collected from the web to learn representations of
actions related with identity, use this knowledge to automatically annotate
identity in videos. Our approach is unsupervised where it can identify the
identity of human in the video like YouTube directly through the knowledge of
his actions. Its benefits are two-fold: 1) we can improve retrieval of identity
images, and 2) we can collect a database of action poses related with identity,
which can then be used in tagging videos. We present the simple experimental
evidence that using action images related with identity collected from the web,
annotating identity is possible.