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Correspondence Analysis and Hierarchical Indexing For Content-Based Image Retrieval

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Published in P. Delogne. IEEE International Conference on Image Processing. Lausanne (Switzerland). 1996, p. 859-862
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 performs ascendant heirarchical classification, an iterative clustering method which constructs a heirarchical index structure for the images of the database. Experimental results supporting the applicability of both techniques to data sets of heterogeneous images are reported.
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Research groups Viper group
Computer Vision and Multimedia Laboratory
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MILANESE, Ruggero, SQUIRE, David, PUN, Thierry. Correspondence Analysis and Hierarchical Indexing For Content-Based Image Retrieval. In: P. Delogne (Ed.). IEEE International Conference on Image Processing. Lausanne (Switzerland). [s.l.] : [s.n.], 1996. p. 859-862. https://archive-ouverte.unige.ch/unige:47838

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