Correspondence analysis and hierarchical indexing for content-based image retrieval

Ruggero Milanese, David Squire, Thierry Pun

Research output: Contribution to conferencePaper

14 Citations (Scopus)

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 languageEnglish
Pages859-862
Number of pages4
Publication statusPublished - 1 Dec 1996
Externally publishedYes
EventProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switzerland
Duration: 16 Sep 199619 Sep 1996

Conference

ConferenceProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)
CountrySwitzerland
CityLausanne
Period16/09/9619/09/96

Cite this

Milanese, R., Squire, D., & Pun, T. (1996). Correspondence analysis and hierarchical indexing for content-based image retrieval. 859-862. Paper presented at Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3), Lausanne, Switzerland.