Analysing and simplifying histograms using scale-trees

S. Gibson; R. W. Harvey

doi:10.1109/ICIAP.2001.956989

Analysing and simplifying histograms using scale-trees

S. Gibson, R. W. Harvey

School of Computing Sciences

Research output: Contribution to conference › Paper

2 Citations (Scopus)

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

Original language	English
Pages	84-89
Number of pages	6
DOIs	https://doi.org/10.1109/ICIAP.2001.956989
Publication status	Published - Sept 2001
Event	11th International Conference on Image Analysis and Processing - Palermo, Italy Duration: 26 Sept 2001 → 28 Sept 2001

Conference

Conference	11th International Conference on Image Analysis and Processing
Country/Territory	Italy
City	Palermo
Period	26/09/01 → 28/09/01

Access to Document

10.1109/ICIAP.2001.956989

Cite this

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title = "Analysing and simplifying histograms using scale-trees",

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",

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