Ruggero Milanese, David McG. Squire and Thierry Pun,
Correspondence Analysis and Hierarchical Indexing For
Content-Based Image Retrieval,
In P. Delogne ed., IEEE International Conference on Image Processing,
Lausanne, Switzerland, Vol. 3, pp. 859-862, September 1996.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 performs 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.