Analysing and simplifying histograms using scale-trees

Author

Gibson, Stuart ; Harvey, Richard

Author_Institution

Sch. of Inf. Syst., East Anglia Univ., Norwich, UK

fYear

2001

Firstpage

84

Lastpage

89

Abstract

A new method for analysing image histograms is introduced. The technique decomposes a histogram into probability level sets. The relationships between these level sets are encoded using a tree. The tree has fewer nodes than the histogram and so is a compressed feature. When used in image retrieval experiments the tree is shown to have a performance that is superior to many methods and no worse than the best alternatives. The tree is efficient because it can be built using a computationally efficient algorithm known as a sieve

Keywords

data compression; feature extraction; image coding; image colour analysis; image resolution; image retrieval; probability; set theory; statistical analysis; tree data structures; tree searching; compressed feature; histogram decomposition; image histogram analysis; image retrieval; probability level sets; scale trees; sieve algorithm; tree encoding; Histograms; Image analysis; Image coding; Image retrieval; Information analysis; Information systems; Level set; Machine vision; Multidimensional systems; Quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Analysis and Processing, 2001. Proceedings. 11th International Conference on

Conference_Location

Palermo

Print_ISBN

0-7695-1183-X

Type

conf

DOI

10.1109/ICIAP.2001.956989

Filename

956989