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.
|Number of pages||4|
|Publication status||Published - 1 Dec 1996|
|Event||Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switzerland|
Duration: 16 Sep 1996 → 19 Sep 1996
|Conference||Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)|
|Period||16/09/96 → 19/09/96|