scholarly journals Permuted Pattern Matching Algorithms on Multi-Track Strings

Algorithms ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 73 ◽  
Author(s):  
Diptarama Hendrian ◽  
Yohei Ueki ◽  
Kazuyuki Narisawa ◽  
Ryo Yoshinaka ◽  
Ayumi Shinohara
Keyword(s):  
Pattern Matching ◽  
Computing Time ◽  
Matching Problem ◽  
Alphabet Size ◽  
Filtering Algorithms ◽  
Dictionary Matching ◽  
Running Time ◽  
Theoretical Computing

A multi-track string is a tuple of strings of the same length. Given the pattern and text of two multi-track strings, the permuted pattern matching problem is to find the occurrence positions of all permutations of the pattern in the text. In this paper, we propose several algorithms for permuted pattern matching. Our first algorithm, which is based on the Knuth–Morris–Pratt (KMP) algorithm, has a fast theoretical computing time with O ( m k ) as the preprocessing time and O ( n k log σ ) as the matching time, where n, m, k, σ , and denote the length of the text, the length of the pattern, the number of strings in the multi-track, the alphabet size, and the number of occurrences of the pattern, respectively. We then improve the KMP-based algorithm by using an automaton, which has a better experimental running time. The next proposed algorithms are based on the Boyer–Moore algorithm and the Horspool algorithm that try to perform pattern matching. These algorithms are the fastest experimental algorithms. Furthermore, we propose an extension of the AC-automaton algorithm that can solve dictionary matching on multi-tracks, which is a task to find multiple multi-track patterns in a multi-track text. Finally, we propose filtering algorithms that can perform permuted pattern matching quickly in practice.

PARALLEL PATTERN MATCHING WITH SCALING

2001 ◽  
Vol 11 (01) ◽  
pp. 125-138 ◽  
Author(s):  
H. MONGELLI ◽  
S. W. SONG
Keyword(s):  
Parallel Algorithms ◽  
Parallel Algorithm ◽  
Pattern Matching ◽  
Computing Time ◽  
Parallel Machine ◽  
Experimental Results ◽  
Coarse Grained ◽  
Matching Problem ◽  
Communication Round ◽  
Parallel Pattern

Given a text and a pattern, the problem of pattern matching consists of determining all the positions of the text where the pattern occurs. When the text and the pattern are matrices, the matching is termed bidimensional. There are variations of this problem where we allow the matching using a somehow modified pattern. A modification that we will allow is that the pattern can be scaled. We propose a new parallel algorithm for this problem, under the CGM (Coarse Grained Multicomputer) model. This algorithm requires linear local computing time in the input, linear memory and uses only one communication round, during which at most a linear amount of data is exchanged. To be the best of our knowledge, there are no known parallel algorithms for the bidimensional pattern matching problem with scaling in the literature. This proposed algorithm was implemented in C, using the PVM interface and was executed on a Parsytec PowerXplorer parallel machine. The experimental results obtained were very promising and showed significant speedups.

scholarly journals An Improved Metric Learning Approach for Degraded Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guofeng Zou ◽  
Yuanyuan Zhang ◽  
Kejun Wang ◽  
Shuming Jiang ◽  
Huisong Wan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Metric Learning ◽  
Computing Time ◽  
Feature Space ◽  
Projection Algorithm ◽  
Learning Approach ◽  
Learning Method ◽  
Matching Problem ◽  
Locality Preserving Projection ◽  
Locality Preserving

To solve the matching problem of the elements in different data collections, an improved coupled metric learning approach is proposed. First, we improved the supervised locality preserving projection algorithm and added the within-class and between-class information of the improved algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. Furthermore, we extended this algorithm to nonlinear space, and the kernel coupled metric learning method based on supervised locality preserving projection is proposed. In kernel coupled metric learning approach, two elements of different collections are mapped to the unified high dimensional feature space by kernel function, and then generalized metric learning is performed in this space. Experiments based on Yale and CAS-PEAL-R1 face databases demonstrate that the proposed kernel coupled approach performs better in low-resolution and fuzzy face recognition and can reduce the computing time; it is an effective metric method.

A new algorithm for the small-field astrometric point-pattern matching problem

2018 ◽  
Vol 72 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Cláudio P. Santiago ◽  
Carlile Lavor ◽  
Sérgio Assunção Monteiro ◽  
Alberto Kroner-Martins
Keyword(s):  
Pattern Matching ◽  
Point Pattern ◽  
Small Field ◽  
Matching Problem ◽  
Point Pattern Matching

scholarly journals Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

2010 ◽  
Vol 20 (1) ◽  
pp. 135-147 ◽  
Author(s):  
Ewa Świercz
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Pattern Matching ◽  
Classification Algorithm ◽  
Statistical Distribution ◽  
Classification Rule ◽  
Matching Problem ◽  
Time Frequency ◽  
Stationary Signals ◽  
Non Stationary Signal

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distributionA new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requiresa prioriknowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to themaxdiscriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

IDPM: An Improved Degenerate Pattern Matching Algorithm for Biological Sequences

2017 ◽  
Vol 28 (07) ◽  
pp. 889-914
Author(s):  
Jie Lin ◽  
Yue Jiang ◽  
E. James Harner ◽  
Bing-Hua Jiang ◽  
Don Adjeroh
Keyword(s):  
Performance Improvement ◽  
Pattern Matching ◽  
Linear Time ◽  
Computational Cost ◽  
Large Data ◽  
Biological Sequences ◽  
Matching Problem ◽  
Practical Utilization ◽  
Matching Algorithm ◽  
Pattern Matching Algorithm

Let [Formula: see text] be a string, with symbols from an alphabet. [Formula: see text] is said to be degenerate if for some positions, say [Formula: see text], [Formula: see text] can contain a subset of symbols from the symbol alphabet, rather than just one symbol. Given a text string [Formula: see text] and a pattern [Formula: see text], both with symbols from an alphabet [Formula: see text], the degenerate string matching problem, is to find positions in [Formula: see text] where [Formula: see text] occured, such that [Formula: see text], [Formula: see text], or both are allowed to be degenerate. Though some algorithms have been proposed, their huge computational cost pose a significant challenge to their practical utilization. In this work, we propose IDPM, an improved degenerate pattern matching algorithm based on an extension of the Boyer–Moore algorithm. At the preprocessing phase, the algorithm defines an alphabet-independent compatibility rule, and computes the shift arrays using respective variants of the bad character and good suffix heuristics. At the search phase, IDPM improves the matching speed by using the compatibility rule. On average, the proposed IDPM algorithm has a linear time complexity with respect to the text size, and to the overall size of the pattern. IDPM demonstrates significance performance improvement over state-of-the-art approaches. It can be used in fast practical degenerate pattern matching with large data sizes, with important applications in flexible and scalable searching of huge biological sequences.

scholarly journals Point Pattern Matching Algorithm for Planar Point Sets under Euclidean Transform

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoyun Wang ◽  
Xianquan Zhang
Keyword(s):  
Pattern Matching ◽  
Complete Graph ◽  
Point Pattern ◽  
Point Sets ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Planar Point ◽  
Point Pattern Matching ◽  
Point Set ◽  
Pattern Matching Algorithm

Point pattern matching is an important topic of computer vision and pattern recognition. In this paper, we propose a point pattern matching algorithm for two planar point sets under Euclidean transform. We view a point set as a complete graph, establish the relation between the point set and the complete graph, and solve the point pattern matching problem by finding congruent complete graphs. Experiments are conducted to show the effectiveness and robustness of the proposed algorithm.

Parallel Algorithms for the Boxed-Mesh Permutation Pattern Matching Problem

2019 ◽  
Vol 46 (4) ◽  
pp. 299-307
Author(s):  
Jihyo Choi ◽  
Youngho Kim ◽  
Joong Chae Na ◽  
Jeong Seop Sim
Keyword(s):  
Parallel Algorithms ◽  
Pattern Matching ◽  
Matching Problem ◽  
Permutation Pattern
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