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Proceedings Paper

Using background knowledge for picture organization and retrieval
Author(s): Yuri Quintana
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Paper Abstract

A picture knowledge base management system is described that is used to represent, organize and retrieve pictures from a frame knowledge base. Experiments with human test subjects were conducted to obtain further descriptions of pictures from news magazines. These descriptions were used to represent the semantic content of pictures in frame representations. A conceptual clustering algorithm is described which organizes pictures not only on the observable features, but also on implicit properties derived from the frame representations. The algorithm uses inheritance reasoning to take into account background knowledge in the clustering. The algorithm creates clusters of pictures using a group similarity function that is based on the gestalt theory of picture perception. For each cluster created, a frame is generated which describes the semantic content of pictures in the cluster. Clustering and retrieval experiments were conducted with and without background knowledge. The paper shows how the use of background knowledge and semantic similarity heuristics improves the speed, precision, and recall of queries processed. The paper concludes with a discussion of how natural language processing of can be used to assist in the development of knowledge bases and the processing of user queries.

Paper Details

Date Published: 15 January 1997
PDF: 10 pages
Proc. SPIE 3022, Storage and Retrieval for Image and Video Databases V, (15 January 1997); doi: 10.1117/12.263438
Show Author Affiliations
Yuri Quintana, Univ. of Western Ontario (Canada)

Published in SPIE Proceedings Vol. 3022:
Storage and Retrieval for Image and Video Databases V
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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