Unsupervised learning of multiple aspects of moving objects from video

Michalis K Titsias, Christopher KI Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A popular framework for the interpretation of image sequences is
based on the layered model; see e.g. Wang and Adelson [8], Irani et al. [2].
Jojic and Frey [3] provide a generative probabilistic model framework for this
task. However, this layered models do not explicitly account for variation due to
changes in the pose and self occlusion. In this paper we show that if the motion
of the object is large so that different aspects (or views) of the object are visible at
different times in the sequence, we can learn appearance models of the different
aspects using a mixture modelling approach.
Original languageEnglish
Title of host publicationAdvances in Informatics
Subtitle of host publication10th Panhellenic Conference on Informatics, PCI 2005, Volas, Greece, November 11-13, 2005. Proceedings
PublisherSpringer
Pages746-756
Number of pages11
ISBN (Electronic)978-3-540-32091-3
ISBN (Print)978-3-540-29673-7
DOIs
Publication statusPublished - 2005

Publication series

NameLecture Notes in Computer Science
Volume3746
ISSN (Print)0302-9743

Fingerprint

Dive into the research topics of 'Unsupervised learning of multiple aspects of moving objects from video'. Together they form a unique fingerprint.

Cite this