Name
Title | ||
|---|---|---|
Full-Search-Equivalent Pattern Matching with Incremental Dissimilarity Approximations |
Abstract | ||
|---|---|---|
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 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 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TPAMI.2008.46 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
approximation theory,image matching,search problems,full search algorithm,full-search-equivalent pattern matching,incremental dissimilarity approximations,sum of absolute differences,sum of squared differences,Computer vision,Pattern analysis,Pattern matching | Kernel (linear algebra),Mathematical optimization,Search algorithm,Upper and lower bounds,Lp space,Approximation theory,Algorithm,Sum of absolute differences,Pattern matching,Mathematics,Bounding overwatch | Journal |
Volume | Issue | ISSN |
31 | 1 | 0162-8828 |
Citations | PageRank | References |
32 | 1.21 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Federico Tombari | 1 | 39 | 1.78 |
| Stefano Mattoccia | 2 | 727 | 52.65 |
| Luigi Di Stefano | 3 | 197 | 11.89 |