Structural pattern recognition using genetic algorithms

Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/s0031-3203(01)00136-4

Cited by 42 publications

(17 citation statements)

References 12 publications

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“…In this case, nodes and edges may have vector attributes or labels. There is a vast literature addressing the graph matching problem in pattern recognition, which can be divided generally into work on search methods [14], [17], [18], [19], [20], [21], [22], [23], and work on nonsearch methods, such as probabilistic relaxation [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], spectral and least-squares methods [5], [36], [37], [38], [39], [40], graduated assignment [13], genetic optimization [41] and other principles [15], [16], [42], [43]. For a recent comprehensive review on graph matching for pattern recognition, see [44].…”

Section: Problem Description and Related Problemsmentioning

confidence: 99%

Graphical Models and Point Pattern Matching

Caetano

Caelli

Schuurmans

et al. 2006

IEEE Trans. Pattern Anal. Mach. Intell.

117

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Abstract-This paper describes a novel solution to the rigid point pattern matching problem in Euclidean spaces of any dimension. Although we assume rigid motion, jitter is allowed. We present a noniterative, polynomial time algorithm that is guaranteed to find an optimal solution for the noiseless case. First, we model point pattern matching as a weighted graph matching problem, where weights correspond to Euclidean distances between nodes. We then formulate graph matching as a problem of finding a maximum probability configuration in a graphical model. By using graph rigidity arguments, we prove that a sparse graphical model yields equivalent results to the fully connected model in the noiseless case. This allows us to obtain an algorithm that runs in polynomial time and is provably optimal for exact matching between noiseless point sets. For inexact matching, we can still apply the same algorithm to find approximately optimal solutions. Experimental results obtained by our approach show improvements in accuracy over current methods, particularly when matching patterns of different sizes.

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Section: Problem Description and Related Problemsmentioning

confidence: 99%

Graphical Models and Point Pattern Matching

Caetano

Caelli

Schuurmans

et al. 2006

IEEE Trans. Pattern Anal. Mach. Intell.

117

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show abstract

“…The two tabular parsing methods for context-free language, the cockekasami-younger ( cky) and the Earley termed.In syntactic pattern recognition ,we assume that a pattern can be exemplified by a sequence of primitives and a class of patterns makes a language L(G).consider two grammars 1 and 2 which represent the class 1 and the class 2,respectively.If a sequence of primitives x belongs to ( 1 ),x is determined to be a pattern of the class 1.if x belongs to neither ( 1 ) and ( 2 ),x is rejected, [1]- [3]. In the syntactic approach ,formal grammars are used for pattern class representation.The productions of a grammar describe how complex (sub)patterns can be built up from simpler elements.The recognition procedure is based on the conceptof formal language parsing.The most fundumental concept is string grammars.They operateon strings of symbols,i.e words over a finite alphabet.Formal grammars operate on words over finite sets of symbols, [1]- [6].…”

Section: Introductionmentioning

confidence: 99%

A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

Bazrafkan¹,

Broumandnia²

2013

IJCA

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The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches:the decision-theoretic approach and the syntactic (structural) approach. In this paper,at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top -down tabular parser known as Earley's algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problemand for this reasontakes a lots of time,searching in array and grammar parsing,and wasting lots of memory. In order to solve these problems and most important,the cubic time complexity,in this article,a new algorithm has been introduced,which reduces wasting the memory to zero, with using linked list data structure.Also, withthe changes in the implementation and performance of the algorithm,cubic time complexity has transformed into O (n*R) order.

show abstract

“…In the syntactic approach ,formal grammars are used for pattern class representation.The productions of a grammar describe how complex (sub)patterns can be built up from simpler elements.The recognition procedure is based on the conceptof formal language parsing.The most fundumental concept is string grammars.They operateon strings of symbols,i.e words over a finite alphabet.Formal grammars operate on words over finite sets of symbols, [1]- [6].…”

Section: Introductionmentioning

confidence: 99%

A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

Bazrafkan¹,

Broumandnia²

2013

IJCA

View full text Add to dashboard Cite

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches:the decision-theoretic approach and the syntactic (structural) approach.In this paper,at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top -down tabular parser known as Earley's algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problemand for this reasontakes a lots of time,searching in array and grammar parsing,and wasting lots of memory. In order to solve these problems and most important,the cubic time complexity,in this article,a new algorithm has been introduced,which reduces wasting the memory to zero, with using linked list data structure.Also, withthe changes in the implementation and performance of the algorithm,cubic time complexity has transformed into O (n*R) order.

show abstract

Structural pattern recognition using genetic algorithms

Cited by 42 publications

References 12 publications

Graphical Models and Point Pattern Matching

Graphical Models and Point Pattern Matching

A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

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