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A Spatial Relationship Method Supports Image Indexing and Similarity Retrieval

A Spatial Relationship Method Supports Image Indexing and Similarity Retrieval
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Author(s): Ying-Hong Wang (Tamkang University, Taiwan)
Copyright: 2008
Pages: 22
Source title: Multimedia Technologies: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Mahbubur Rahman Syed (Minnesota State University Mankato, USA)
DOI: 10.4018/978-1-59904-953-3.ch112

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Abstract

The increasing availability of image and multimedia-oriented applications markedly impacts image/multimedia file and database systems. Image data are not well-defined keywords such as traditional text data used in searching and retrieving functions. Consequently, various indexing and retrieving methodologies must be defined based on the characteristics of image data. Spatial relationships represent an important feature of objects (called icons) in an image (or picture). Spatial representation by 2-D String and its variants, in a pictorial spatial database, has been attracting growing interest. However, most 2-D Strings represent spatial information by cutting the icons out of an image and associating them with many spatial operators. The similarity retrievals by 2-D Strings require massive geometric computation and focus only on those database images that have all the icons and spatial relationships of the query image. This study proposes a new spatial-relationship representation model called “Two Dimension Begin-End boundary string” (2D Be-string). The 2D Be-string represents an icon by its MBR boundaries. By applying “dummy objects,” the 2D Be-string can intuitively and naturally represent the pictorial spatial information without any spatial operator. A method of evaluating image similarities, based on the modified “Longest Common Subsequence” (LCS) algorithm, is presented. The proposed evaluation method can not only sift out those images of which all icons and their spatial relationships fully accord with query images, but for those images whose icons and/or spatial relationships are similar to those of query images. Problems of uncertainty in the query targets and/or spatial relationships thus solved. The representation model and similarity evaluation also simplify the retrieval progress of linear transformations, including rotation and reflection, of images.

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