A Hierarchical Approach to Efficient Curvilinear Object Searching

Curvilinear object searching is a common problem encountered in pattern recognition and information retrieval. How to improve the efficiency of searching is the major concern, especially when the data set is large. In this paper we propose a hierarchical approach, where high-level, salient shape features of various types are extracted and used to represent curvilinear objects at different levels of abstraction. The searching process is carried out top-down?first at the top level where only numbers of features of the same type are compared, then at the middle level where the geometric constraints among the features are checked, and finally at the bottom level where the parts between the features are considered. The searching space is reduced at each level and finally the most extensive matching operation needs to be applied to only a restricted set of candidates, thus achieving high efficiency. The general scheme has been implemented in two different applications, road image matching and cursive handwriting recognition. Experimental results from both applications are reported. Guidelines for feature selection are also provided to facilitate adaptation of the general scheme to other applications.