Recognition and retrieval via histogram trees

S. Gibson; R. W. Harvey

Recognition and retrieval via histogram trees

Research output: Contribution to conference › Paper

Abstract

This paper explores a new method for analysing and comparing image histograms. The technique amounts to a novel way of backprojecting an image into one with fewer, statistically significant colours. When the method is tested with the COIL-100 and MPEG-7 data sets it is shown to have a performance that is as good as the best methods using fewer enteries than the original histogram. Therefore it offers the potential for extending the use of histograms into high dimensional feature spaces.

Original language	English
Pages	531-540
Number of pages	10
Publication status	Published - Sep 2001
Event	The British Machine Vision Conference - Manchester, United Kingdom Duration: 10 Sep 2001 → 13 Sep 2001

Conference

Conference	The British Machine Vision Conference
Country/Territory	United Kingdom
City	Manchester
Period	10/09/01 → 13/09/01

Access to Document

http://www.bmva.org/bmvc/2001/papers/59/59.html

Cite this

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title = "Recognition and retrieval via histogram trees",

abstract = "This paper explores a new method for analysing and comparing image histograms. The technique amounts to a novel way of backprojecting an image into one with fewer, statistically significant colours. When the method is tested with the COIL-100 and MPEG-7 data sets it is shown to have a performance that is as good as the best methods using fewer enteries than the original histogram. Therefore it offers the potential for extending the use of histograms into high dimensional feature spaces.",

author = "S. Gibson and Harvey, {R. W.}",

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AU - Harvey, R. W.

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AB - This paper explores a new method for analysing and comparing image histograms. The technique amounts to a novel way of backprojecting an image into one with fewer, statistically significant colours. When the method is tested with the COIL-100 and MPEG-7 data sets it is shown to have a performance that is as good as the best methods using fewer enteries than the original histogram. Therefore it offers the potential for extending the use of histograms into high dimensional feature spaces.

M3 - Paper

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EP - 540

T2 - The British Machine Vision Conference

Y2 - 10 September 2001 through 13 September 2001

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