Constructing Visual Taxonomies by Shape

Anthony Cook, M. J. Gibbens

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

1 Citation (SciVal)

Abstract

We investigate the use of statistical shape measures for segmented image regions to construct taxonomies of visual similarity. It is demonstrated that without the use of a priori knowledge, cluster analysis can be used to impose structure on heterogeneous image data sets. We develop visual taxonomies to accomplish moderate classification tasks, and provide a framework for more powerful, open-ended analysis of large data sets. The power of this method is demonstrated using a visual taxonomy of textual data, which is shown to be efficient in an MDL context
Original languageEnglish
Title of host publication18th International Conference on Pattern Recognition, 2006. ICPR 2006
PublisherIEEE Press
Pages732 - 735
Number of pages4
ISBN (Print)0-7695-2521-0
DOIs
Publication statusPublished - 2006

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