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Proceedings Paper

Mining association rules between low-level image features and high-level concepts
Author(s): Ishwar K. Sethi; Ioana L. Coman; Daniela Stan
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Paper Abstract

In image similarity retrieval systems, color is one of the most widely used features. Users who are not well versed with the image domain characteristics might be more comfortable in working with an Image Retrieval System that allows specification of a query in terms of keywords, thus eliminating the usual intimidation in dealing with very primitive features. In this paper we present two approaches to automatic image annotation, by finding those rules underlying the links between the low-level features and the high-level concepts associated with images. One scheme uses global color image information and classification tree based techniques. Through this supervised learning approach we are able to identify relationships between global color-based image features and some textual decriptors. In the second approach, using low-level image features that capture local color information and through a k-means based clustering mechanism, images are organized in clusters such that images that are similar are located in the same cluster. For each cluster, a set of rules is derived to capture the association between the localized color-based image features and the textual descriptors relevant to the cluster.

Paper Details

Date Published: 27 March 2001
PDF: 12 pages
Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421083
Show Author Affiliations
Ishwar K. Sethi, Oakland Univ. (United States)
Ioana L. Coman, Syracuse Univ. (United States)
Daniela Stan, Oakland Univ. (United States)


Published in SPIE Proceedings Vol. 4384:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology III
Belur V. Dasarathy, Editor(s)

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