TED: A Tolerant Edit Distance for segmentation evaluation

Methods ◽

10.1016/j.ymeth.2016.12.013 ◽

2017 ◽

Vol 115 ◽

pp. 119-127 ◽

Cited By ~ 5

Author(s):

Jan Funke ◽

Jonas Klein ◽

Francesc Moreno-Noguer ◽

Albert Cardona ◽

Matthew Cook

Keyword(s):

Edit Distance ◽

Segmentation Evaluation

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Differential Edit Distance: A Metric for Scene Segmentation Evaluation

IEEE Transactions on Circuits and Systems for Video Technology ◽

10.1109/tcsvt.2011.2181231 ◽

2012 ◽

Vol 22 (6) ◽

pp. 904-914 ◽

Cited By ~ 10

Author(s):

Panagiotis Sidiropoulos ◽

Vasileios Mezaris ◽

Ioannis Kompatsiaris ◽

Josef Kittler

Keyword(s):

Edit Distance ◽

Scene Segmentation ◽

Segmentation Evaluation

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Edit distance computation with minimum number of edit operations in database management system and information retrieval

International Journal of Scientific and Research Publications (IJSRP) ◽

10.29322/ijsrp.9.09.2019.p9385 ◽

2019 ◽

Vol 9 (9) ◽

pp. p9385

Author(s):

Yi Mar Myint

Keyword(s):

Information Retrieval ◽

Management System ◽

Database Management ◽

Edit Distance ◽

Database Management System ◽

Distance Computation ◽

Minimum Number

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Stochastic Contextual Edit Distance and Probabilistic FSTs

10.3115/v1/p14-2102 ◽

2014 ◽

Cited By ~ 1

Author(s):

Ryan Cotterell ◽

Nanyun Peng ◽

Jason Eisner

Keyword(s):

Edit Distance

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Word Similarity Calculation by Using the Edit Distance Metrics with Consonant Normalization

Journal of Information Processing Systems ◽

10.3745/jips.04.0018 ◽

2015 ◽

Keyword(s):

Edit Distance ◽

Distance Metrics ◽

Word Similarity ◽

Similarity Calculation

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On the unification of the graph edit distance and graph matching problems

Pattern Recognition Letters ◽

10.1016/j.patrec.2021.02.014 ◽

2021 ◽

Vol 145 ◽

pp. 240-246

Author(s):

Romain Raveaux

Keyword(s):

Edit Distance ◽

Graph Matching ◽

Graph Edit Distance ◽

Matching Problems

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A contextual normalised edit distance

2008 IEEE 24th International Conference on Data Engineering Workshop ◽

10.1109/icdew.2008.4498345 ◽

2008 ◽

Cited By ~ 1

Author(s):

Colin de la Higuera ◽

Luisa Mico

Keyword(s):

Edit Distance

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On-line learning the graph edit distance costs

Pattern Recognition Letters ◽

10.1016/j.patrec.2021.02.019 ◽

2021 ◽

Author(s):

Elena Rica ◽

Susana Álvarez ◽

Francesc Serratosa

Keyword(s):

Edit Distance ◽

Graph Edit Distance ◽

On Line ◽

On Line Learning

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Suffix array for multi-pattern matching with variable length wildcards

Intelligent Data Analysis ◽

10.3233/ida-205087 ◽

2021 ◽

Vol 25 (2) ◽

pp. 283-303

Author(s):

Na Liu ◽

Fei Xie ◽

Xindong Wu

Keyword(s):

Dynamic Programming ◽

Data Structure ◽

Pattern Matching ◽

Edit Distance ◽

State Of The Art ◽

Suffix Array ◽

Variable Length ◽

Distance Method ◽

Efficient Data ◽

Comparison Algorithms

Approximate multi-pattern matching is an important issue that is widely and frequently utilized, when the pattern contains variable-length wildcards. In this paper, two suffix array-based algorithms have been proposed to solve this problem. Suffix array is an efficient data structure for exact string matching in existing studies, as well as for approximate pattern matching and multi-pattern matching. An algorithm called MMSA-S is for the short exact characters in a pattern by dynamic programming, while another algorithm called MMSA-L deals with the long exact characters by the edit distance method. Experimental results of Pizza & Chili corpus demonstrate that these two newly proposed algorithms, in most cases, are more time-efficient than the state-of-the-art comparison algorithms.

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