Refereed full papers (journals, book chapters, international conferences)

1997

@inproceedings{CoG1997,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{S. D. Cohen and L. J. Guibas},
	title =	{Shape-based image retrieval using geometric hashing},
	booktitle =	{Proceedings of the ARPA Image Understanding Workshop},
	pages =	{669--674},
	month =	{May},
	year =	{1997},
	abstract =	{We present a general strategy for \emph{shape-based image
	retrieval} which considers similarity modulo a given transformation
	group ${\cal G}$. The shape content of an image is summarized by
	recording what geometric primitives, such as line segments and circular
	arcs, fit where in the image. Geometric hashing is used to compute a
	set of primitive features which are invariant under a ${\cal
	G}$-transformation of the image. Our search engine is
	\emph{feature-based} in the sense that similarity is determined by
	looping over the features in the query and asking: Which database
	images have features that are close to a given query? The most similar
	database images are the ones that have many features which are close to
	query features. We apply our approach to an example database of 500
	Chinese character bitmaps.},
}