Full-Search-Equivalent Pattern Matching with Incremental Dissimilarity Approximations.

This paper proposes a novel method for fast pattern matching based on dissimilarity functions derived from the Lp norm, such as the Sum of Squared Differences (SSD) and the Sum of Absolute Differences (SAD). The proposed method is a full-search equivalent, i.e., it yields the same results as the Full Search (FS) algorithm. In order to pursue computational savings, the method deploys a succession of increasingly tighter lower bounds of the adopted Lp norm-based dissimilarity function. Such bounding functions allow for establishing a hierarchy of pruning conditions aimed at rapidly skipping those candidates that cannot satisfy the matching criterion. The paper includes an experimental comparison between the proposed method and other FS-equivalent approaches known in the literature, which proves the remarkable computational efficiency of our proposal. [ABSTRACT FROM AUTHOR]