Adaptive Metadata Generation for Integration of Visual and Semantic Information

Adaptive Metadata Generation for Integration of Visual and Semantic Information

Hideyasu Sasaki (Ritsumeikan University, Japan) and Yasushi Kiyoki (Keio University, Japan)
Copyright: © 2007 |Pages: 25
DOI: 10.4018/978-1-59904-370-8.ch007
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Abstract

The principal concern of this chapter is to provide those in the visual information retrieval community with a methodology which allows them to integrate the results of content analysis of visual information, i.e., the content descriptors, and their text-based representation to attain the semantically precise results of keyword-based image retrieval operations. The main visual objects of our discussion are images which do not have any semantic representations therein. Those images demand textual annotation of precise semantics which is to be based on the results of automatic content analysis but not on the results of time-consuming manual annotation processes. We first outline the technical background and literature review on a variety of annotation techniques for visual information retrieval. We then describe our proposed method and its implemented system for generating metadata or textual indexes to visual objects by using content analysis technique by bridging the gaps between content descriptors and textual information.

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