@techreport{TD:101608,
	att_abstract={{We address the problem of estimating place information
of an image using principles from automated representation
learning. We pursue a hierarchical sparse coding approach
that learns features useful in discriminating images
across places, by initializing it with a geometric prior corresponding
to transformations between image appearance
space and their corresponding place grouping space using
the notion of parallel transport on manifolds. We then extend
this approach to account for the availability of heterogeneous
data modalities such as place tags and videos
pertaining to different places, and also study a relatively
under-addressed problem of transferring knowledge available
from certain places to infer the grouping of data from
novel places. We evaluate our approach on several standard
datasets such as im2gps, San Francisco and MediaEval2010,
and obtain state-of-the-art results}},
	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 2015. {{, 2015-06-08}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101608},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101608_DS1_2015-04-07T15:29:07.435Z.pdf},
	author={Raghuraman Gopalan},
	institution={{IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)}},
	month={June},
	title={{Hierarchical Sparse Coding With Geometric Prior For Visual Place Recognition}},
	year=2015,
}