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

1999

@inproceedings{LiS1999,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Xiaonong Li and Boaz J. Super},
	title =	{Fast Shape Retrieval Using Term Frequency Vectors},
	booktitle =	{IEEE Workshop on Content-based Access of Image and Video
	Libraries (CBAIVL'99)},
	address =	{Fort Collins, Colorado, USA},
	pages =	{18--22},
	month =	{June~22},
	year =	{1999},
	abstract =	{Similarity-based retrieval of visual shapes is performed
	using a vector space technique originally developed for document
	retrieval. In that technique, a document is represented by a vector in
	which component is the frequency of occurence of a specific term (word
	or phrase) in that document. Similarity between query and database
	documents is measured by the normalized inner product of the vectors.
	In the shape retrieval system, contours are partitioned into
	perceptually significant segments that have a role analogous to words
	in a document. Short sequences of segments are analogous to phrases.
	Each shape is represented by a vector of the frequency of occurences of
	each term (segments or segment sequences). Fast retrieval is achieved
	by using a $B+$ tree and inverted lists. Additionally, a new similarity
	measure is introduced and shown to result in improved performance over
	the normalized inner product},
}