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

2002

@inproceedings{SNF2002,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Renato O. Stehling and Mario A. Nascimento and Alexandre
	X.  Falc\~{a}o},
	title =	{Mi{CR}o{M}: A Metric Distance to Compare Segmented Images},
	editor =	{Shi-Kuo Chang and Zen Chen and Suh-Yin Lee},
	booktitle =	{Proceedings of the 5th International Conference on Recent
	Advances in Visual Information Systems (VISUAL 2002)},
	address =	{Hsin Chu, Taiwan},
	number =	{2314},
	series =	{Lecture Notes in Computer Science},
	pages =	{12--23},
	publisher =	{Springer-Verlag},
	month =	{March~11--13},
	year =	{2002},
	url =	{http://www.springerlink.com/link.asp?id=w01b3mar7xm3dglc},
	abstract =	{Recently, several content-based image retrieval (CBIR)
	systems that make use of segmented images have been proposed. In these
	systems, images are segmented and represented as a set of regions, and
	the distance between images is computed according to the visual
	features of their regions. A major problem of existing distance
	functions used to compare segmented images is that they are not
	metrics. Hence, it is not possible to exploit filtering techniques
	and/or access methods to speedup query processing, as both techniques
	make extensive use of the triangular inequality property - one of the
	metric axioms. In this work, we propose MiCRoM (Minimum-Cost Region
	Matching), an effective metric distance which models the comparison of
	segmented images as a  minimum-cost network flow problem. To our
	knowledge, this is the first time a true metric distance function is
	proposed to evaluate the distance between segmented images. Our
	experiments show that MiCRoM is at least as effective as existing
	non-metric distances. Moreover, we have been able to use the recently
	proposed Omni-sequential filtering technique, and have achieved nearly
	2/3 savings in retrieval/query processing time.},
}