@techreport{TD:101945,
	att_abstract={{Predicting the temporal evolution of images is an
interesting problem that has applications in surveillance, content
recommendation and behavioral analysis. Given a single image
or a stream of images with timestamps, the goal of this work
is to predict possible images that could appear at different time
instances in the future. We propose a data-driven Riemannian
shape theoretic approach for this problem, which analyzes the
space of temporal evolution patterns in training image streams,
and performs statistics on this shape space to facilitate future
image prediction. We consider both discriminative and generative
statistical techniques on the shape space, to accommodate cases
where the training and test data may or may not have an
associated class/topic label, and report improved prediction
results on two previously studied image prediction datasets. We
also provide complimentary results on predicting images in the
past, for time instances before the training data was acquired,
and empiri}},
	att_authors={rg4675},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE. {{, 2017-04-01}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101945},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101945_DS1_2016-12-30T01:51:05.688Z.pdf},
	author={Raghuraman Gopalan},
	institution={{IEEE}},
	month={April},
	title={{Walking On The Image Trail: Uncovering The Past And Predicting The Future}},
	year=2017,
}