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Proceedings Paper

Content-based indexing technique using relative geometry features
Author(s): Tai-Yuan Hou; Arding Hsu; Peiya Liu; Ming-Yee Chiu
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Paper Abstract

Content-based image indexing is becoming increasingly important in today''s imaging applications. Although several techniques have been developed, most have limited capabilities in dealing with relative spatial relations for multiple object images. We believe that multiple object image indexing is important for content-based indexing in many application domains. We propose a content-based indexing technique which is based on the theory of weighted center-of-mass. The index of an image is constructed by incorporating the center-of-mass of each individual object to calculate the center-of-mass of the overall image. This multiple object indexing scheme is further enhanced by incorporating existing indexing structure construction techniques, such as Jagadish''s multidimensional point representation, and Grosky''s feature index tree. An image with multiple objects can be retrieved by specifying the spatial relations among image objects, such as relative position, relative distance, and direction. The retrieval matching is based on a relative similarity measure. Furthermore, due to certain invariant properties of center-of-mass, different notions of image similarity with respect to scaling, translation, rotation, and mirrored image can be easily supported. The advantages of the proposed method are illustrated with an example of retrieving magnetic resonance images of patients'' chests within the medical image domain. The relationships to certain other indexing techniques are also addressed.

Paper Details

Date Published: 1 April 1992
PDF: 10 pages
Proc. SPIE 1662, Image Storage and Retrieval Systems, (1 April 1992); doi: 10.1117/12.58493
Show Author Affiliations
Tai-Yuan Hou, Siemens Corporate Research, Inc. (United States)
Arding Hsu, Siemens Corporate Research, Inc. (United States)
Peiya Liu, Siemens Corporate Research, Inc. (United States)
Ming-Yee Chiu, Siemens Corporate Research, Inc. (United States)

Published in SPIE Proceedings Vol. 1662:
Image Storage and Retrieval Systems
Albert A. Jamberdino; Carlton Wayne Niblack, Editor(s)

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