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Proceedings Paper

MALCBR: content-based retrieval of image databases at multiple abstraction levels
Author(s): Vittorio Castelli; Chung-Sheng Li; Lawrence D. Bergman
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Paper Abstract

Content-based search of large image database has received significant attention recently. In this paper, we proposed a new framework, multiple abstraction level content based retrieval, for specifying and process content-based retrieval queries on databases of images, time series, or video data. This framework allows search targets to be expressed in a object-based fashion, that allows the extensible specification of arbitrarily complex queries. In our approach, the search targets are either simple objects, specified at multiple levels of abstraction, or composite objects, defined as collections of relation on the elements of a set of simple objects. During the search, simple objects at the semantic level are retrieved from database tables, feature level objects are computed using pre-extracted features, appropriately indexed, and pixel level objects are extracted from the raw data. Composite objects are computed at query execution time. This framework, provides a powerful mechanism for specifying complicated search target and enable efficient processing of filtering of the search results.

Paper Details

Date Published: 6 October 1997
PDF: 10 pages
Proc. SPIE 3229, Multimedia Storage and Archiving Systems II, (6 October 1997); doi: 10.1117/12.290345
Show Author Affiliations
Vittorio Castelli, IBM Thomas J. Watson Research Ctr. (United States)
Chung-Sheng Li, IBM Thomas J. Watson Research Ctr. (United States)
Lawrence D. Bergman, IBM Thomas J. Watson Research Ctr. (United States)


Published in SPIE Proceedings Vol. 3229:
Multimedia Storage and Archiving Systems II
C.-C. Jay Kuo; Shih-Fu Chang; Venkat N. Gudivada, Editor(s)

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