@conference {12499,
	title = {Nearest-neighbor search algorithms on non-Euclidean manifolds for computer vision applications},
	booktitle = {Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing},
	series = {ICVGIP {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {282 - 289},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Nearest-neighbor searching is a crucial component in many computer vision applications such as face recognition, object recognition, texture classification, and activity recognition. When large databases are involved in these applications, it is also important to perform these searches in a fast manner. Depending on the problem at hand, nearest neighbor strategies need to be devised over feature and model spaces which in many cases are not Euclidean in nature. Thus, metrics that are tuned to the geometry of this space are required which are also known as geodesics. In this paper, we address the problem of fast nearest neighbor searching in non-Euclidean spaces, where in addition to dealing with the large size of the dataset, the significant computational load involves geodesic computations. We study the applicability of the various classes of nearest neighbor algorithms toward this end. Exact nearest neighbor methods that rely solely on the existence of a metric can be extended, albeit with a huge computational cost. We derive an approximate method of searching via approximate embeddings using the logarithmic map. We study the error incurred in such an embedding and show that it performs well in real experiments.},
	keywords = {Grassmann manifold, hashing, manifold, nearest-neighbor, region covariance, shapes},
	isbn = {978-1-4503-0060-5},
	doi = {10.1145/1924559.1924597},
	url = {http://doi.acm.org/10.1145/1924559.1924597},
	author = {Turaga,Pavan and Chellapa, Rama}
}
@book {13327,
	title = {Shape Analysis and Structuring},
	year = {2008},
	month = {2008///},
	publisher = {Springer},
	organization = {Springer},
	abstract = {With a lot of recent developments in the field, this much-needed book has come at just the right time. It covers a variety of topics related to preserving and enhancing shape information at a geometric level. The contributors also cover subjects that are relevant to effectively capturing the structure of a shape by identifying relevant shape components and their mutual relationships.},
	keywords = {Computer Graphics, Computer vision, Computers / Computer Graphics, Computers / Image Processing, Geometrical models, Geometry, Geometry, Analytic, Image analysis, IMAGE PROCESSING, Mathematics / Functional Analysis, Mathematics / Geometry / General, Mathematics / Graphic Methods, Mathematics / Mathematical Analysis, shapes, Technology \& Engineering / Engineering (General), Visualization},
	isbn = {9783540332640},
	author = {De Floriani, Leila and Spagnuolo,Michela}
}
