Extended Attributed String Matching for Shape Recognition

In this paper, we extend the attributed string matching (ASM) technique, which originally dealt with single objects, to handle scenes containing multiple objects. The emerging issues have uncovered several weaknesses inherent in ASM. We overcome these weaknesses in this study. Major tasks include the introduction of an invariant two-way relaxation process with fuzzy split-and-merge mechanism, a new set of cost functions for edit operators, and the legality costs of edit operations. Three algorithms have been developed, respectively, implementing the original ASM, its modification (MASM) characterized by the proposed new cost functions, and extended ASM (EASM) further incorporating the legality costs of edit operations. These algorithms are then applied to a number of real images. By comparing their performances, we observe that both the new cost functions and the legality costs of edit operations have greatly enlarged the range of the computed similarity values. An augmentation in the separability of similarity values signifies an increment in the discernibility among objects. Experimental results support the applicability of the extended ASM.