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

1995

@inproceedings{KCH1995,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Patrick M. Kelly and Michael Cannon and Donald R. Hush},
	title =	{Query by image example: the {CANDID} approach},
	editor =	{Wayne Niblack and Ramesh C. Jain},
	booktitle =	{Storage and Retrieval for Image and Video Databases III},
	volume =	{2420},
	series =	{SPIE Proceedings},
	pages =	{238--248},
	month =	{March},
	year =	{1995},
	abstract =	{\textbf{CANDID} (Comparison Algorithm for Navigating
	Digital Image Databases) was developed to enable content-based
	retrieval of digital imagery from large databases using a
	query-by-example methodology.  A user provides an example image to the
	system, and images in the database that are similar to that example are
	retrieved. The development of \textbf{CANDID} was inspired by the
	N-gram approach to document fingerprinting, where a ``global
	signature'' is computed for every document in a database and these
	signatures are compared to one another to determine the similarity
	between any two documents. \textbf{CANDID} computes a global signature
	for every image in a database, where the signature is derived from
	various image features such as localized texture, shape, or color
	information. A distance between probability density functions of
	feature vectors is then used to compare signatures. In this paper, we
	present \textbf{CANDID} and highlight two results from our current
	research: subtracting a ``background'' signature from every signature
	in a database in an attempt to improve system performance when using
	inner-product similarity measures, and visualizing the contribution of
	individual pixels in the matching process. These ideas are applicable
	to any histogram-based comparison technique.},
}