Correspondence analysis and hierarchical indexing for content-based image retrieval

Ruggero Milanese; David Squire; Thierry Pun

Correspondence analysis and hierarchical indexing for content-based image retrieval

Ruggero Milanese
, David Squire
, Thierry Pun

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper describes a two-stage statistical approach supporting content-based search in image databases. The first stage performs correspondence analysis, a factor analysis method transforming image attributes into a reduced-size, uncorrelated factor space. The second stage perform ascendant hierarchical classification, an iterative clustering method which constructs a hierarchical index structure for the images of the database. Experimental results supporting the applicability of both techniques to data sets of heterogeneous images are reported.

Original language	English
Pages	859-862
Number of pages	4
Publication status	Published - 1 Dec 1996
Externally published	Yes
Event	IEEE International Conference on Image Processing 1996 - Lausanne, Switzerland Duration: 16 Sept 1996 → 19 Sept 1996 Conference number: 1996 https://ieeexplore.ieee.org/xpl/conhome/4140/proceeding?isnumber=12183 (Proceedings)

Conference

Conference	IEEE International Conference on Image Processing 1996
Abbreviated title	ICIP 1996
Country/Territory	Switzerland
City	Lausanne
Period	16/09/96 → 19/09/96
Internet address	https://ieeexplore.ieee.org/xpl/conhome/4140/proceeding?isnumber=12183 (Proceedings)

Cite this

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title = "Correspondence analysis and hierarchical indexing for content-based image retrieval",

abstract = "This paper describes a two-stage statistical approach supporting content-based search in image databases. The first stage performs correspondence analysis, a factor analysis method transforming image attributes into a reduced-size, uncorrelated factor space. The second stage perform ascendant hierarchical classification, an iterative clustering method which constructs a hierarchical index structure for the images of the database. Experimental results supporting the applicability of both techniques to data sets of heterogeneous images are reported.",

author = "Ruggero Milanese and David Squire and Thierry Pun",

year = "1996",

month = dec,

day = "1",

language = "English",

pages = "859--862",

note = "IEEE International Conference on Image Processing 1996, ICIP 1996 ; Conference date: 16-09-1996 Through 19-09-1996",

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TY - CONF

T1 - Correspondence analysis and hierarchical indexing for content-based image retrieval

AU - Milanese, Ruggero

AU - Squire, David

AU - Pun, Thierry

N1 - Conference code: 1996

PY - 1996/12/1

Y1 - 1996/12/1

N2 - This paper describes a two-stage statistical approach supporting content-based search in image databases. The first stage performs correspondence analysis, a factor analysis method transforming image attributes into a reduced-size, uncorrelated factor space. The second stage perform ascendant hierarchical classification, an iterative clustering method which constructs a hierarchical index structure for the images of the database. Experimental results supporting the applicability of both techniques to data sets of heterogeneous images are reported.

AB - This paper describes a two-stage statistical approach supporting content-based search in image databases. The first stage performs correspondence analysis, a factor analysis method transforming image attributes into a reduced-size, uncorrelated factor space. The second stage perform ascendant hierarchical classification, an iterative clustering method which constructs a hierarchical index structure for the images of the database. Experimental results supporting the applicability of both techniques to data sets of heterogeneous images are reported.

UR - https://www.scopus.com/pages/publications/0030389428

M3 - Paper

AN - SCOPUS:0030389428

SP - 859

EP - 862

T2 - IEEE International Conference on Image Processing 1996

Y2 - 16 September 1996 through 19 September 1996

ER -

Correspondence analysis and hierarchical indexing for content-based image retrieval

Abstract

Conference

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Cite this