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 L
_p
norm, such as the
Sum

of

Squared

Differences
(SSD) and the
Sum

of

Absolute

Differences
(SAD). The proposed method is 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 L
_p
norm-based dissimilarity function. Such bounding functions allow for establishing a hierarchy of pruning conditions aimed at skipping rapidly those candidates that cannot satisfy the matching criterion. The paper includes an experimental comparison between the proposed method and other full-search equivalent approaches known in literature, which proves the remarkable computational efficiency of our proposal.