ARTICLE NO. VC980377 Similarity Retrieval by 2D C-Trees Matching in Image Databases

Logical features are abstract representations of images at various levels of detail. Some logical features such as spa-The image retrieval based on spatial content is an attracting task in many image database applications. The 2D strings pro- tial-location and spatial-relation [18] may be synthesized vide a natural way of constructing spatial indexing for images from primitive features, whereas others can only be ob-and support effective picture query. Nevertheless, the 2D string tained through considerable human involvement. Spatial is deficient in describing the spatial knowledge of nonzero sized constraint is a significant logical feature and is our focus objects with overlapping. In this paper, we use an ordered in this article. labeled tree, a 2D C-tree, to be the spatial representation for The intelligent image database system (IIDS) [4] pro-images and propose the tree-matching algorithm for similarity vides high-level object-oriented search and supports spatialretrieval. The distance between 2D C-trees is used to measure query. The spatial reasoning is based on a data to structurethe similarity of images. The proposed tree comparison algo-called 2D string [3] which preserves the objects ’ spatialrithm is also modified to compute the partial tree distance knowledge embedded in images. Each symbolic picturefor subpicture query. Experimental results for verifying the can be represented by a 2D string and a picture query caneffectiveness of similarity retrieval by 2D C-trees matching are also be specified by a 2D string. The problem of imagepresented. 1998 Academic Press retrieval then becomes a problem of 2D string subsequence matching [15]. Lee and Hsu [13] proposed 2D C-strin