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structural pattern recognition using genetic algorithms

pattern recognition 2002年第9期35卷 1883-1893页

作者： Suganthan, PN Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper presents a genetic algorithm (GA) based optimization procedure for the solution of structural pattern recognition problem using the attributed relational graph representation and matching technique. In this study, candidate solutions are represented by integer strings and the population is randomly initialized. The GA is employed to generate a monomorphic mapping. As all the mapping constraints are not enforced during the search phase in order to speedup the search, an efficient pose clustering algorithm is used to eliminate spurious matches and to determine the presence of the model in the scene. The performance of the proposed approach to pattern recognition by subgraph isomorphism is demonstrated using line patterns and silhouette images. (C) 2002 pattern recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： structural pattern recognition attributed relational graph matching subgraph isomorphism genetic algorithms shape recognition

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structural pattern-recognition, HOMOMORPHISMS, AND ARRANGEMENTS

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pattern recognition 1978年第3期10卷 223-236页

作者： HARALICK, RM UNIV KANSAS DEPT COMP SCI LAWRENCE KS 66045 USA

This paper discusses the general supervised pattern discrimination problem from a structural point of view. We show that both the problem of determining a decision rule and the problem of applying a decision rule are problems of finding homomorphisms, whether the pattern data structure is an N-tuple as in statistical pattern recognition, or a string or its generalizations as in syntactic pattern recognition. We then introduce the concept of an arrangement, which is a labeled N-ary relation, as a more complex pattern data structure and show how decision rules can be constructed and applied to arrangements using the homomorphism concept. The methodology suggested in the paper provides a structural pattern recognition generalization to phrase-structured syntactic pattern recognition.

关键词： pattern inference Grammatical inference Statistical pattern recognition Syntactic pattern recognition Theory of covers Homomorphisms Relations Arrangements structural pattern recognition

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structural pattern recognition methods based on string comparison for fusion databases

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FUSION ENGINEERING AND DESIGN 2008年第2-3期83卷 421-424页

作者： Dormido-Canto, S. Farias, G. Dormido, R. Vega, J. Sanchez, J. Duro, N. Vargas, H. Ratta, G. Pereira, A. Portas, A. Univ Nacl Educ Distancia Dpto Informat & Automat Madrid 28040 Spain CIEMAT Fus EURATOM Assoc Madrid 28040 Spain

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed. (C) 2007 Elsevier B.V. All rights reserved.

关键词： structural pattern recognition data mining nuclear fusion information retrieval

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NLP-inspired structural pattern recognition in chemical application

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pattern recognition LETTERS 2014年第1期45卷 11-16页

作者： Sidorova, J. Anisimova, M. Swiss Fed Inst Technol Swiss Fed Inst Technol Dept Comp Sci Computat Biochem Res Grp Zurich Switzerland Swiss Inst Bioinformat Zurich Switzerland

In this paper we report on a new structural pattern recognition approach for in silico prediction of chemical activity. It is based on grammatical inference on strings representing chemical compounds and string edit distance between a chemical compound and a formal grammar generalizing an activity class. In the late 1980s Weininger published a chemical language with a very simple and natural grammar. Recently, the algorithms suitable to process this language have been developed. From modeling of chemical activity with formal grammars and chemical compounds as words, a functionality is derivable to search for "structural alerts", that is, molecular substructures and their combinatorial patterns that cause a molecule to have properties of interest. A biodegradability prediction system has been constructed to serve as an example throughout the paper. The source code and various files from the experiment are available from the corresponding author on request. (C) 2014 Elsevier B.V. All rights reserved.

关键词： structural pattern recognition Grammar inference Natural language processing Chemical descriptors SMILES Activity prediction

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Continuous Car Driving Intent Detection Using structural pattern recognition

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第2期22卷 1001-1013页

作者： Lee, Sukhan Khan, Muhammad Qasim Husen, Mohd Nizam Sungkyunkwan Univ SKKU Intelligent Syst Res Inst Dept Elect & Comp Engn Suwon 440746 South Korea Univ Kuala Lumpur Malaysian Inst Informat Technol UniKL MIIT Kuala Lumpur 50250 Malaysia

The early detection of a driver's intention prior to the initiation of actual maneuvering is to offer effective means of assisting the driver in times of safety. Conventional approaches in predicting a driver's intention include framing the motion parameters of the car and driver as well as the eye gaze sequence of the driver into the Hidden Markov Model (HMM) for analysis. However, their power of describing driver behavior is not only limited but also their performance, indicated by the recognition rate relative to the amount of preceding time in early detection, is not satisfactory and needs further improvement. This paper presents an approach for early detection of a driver's intention by modeling and analyzing driver behavior by using structural pattern recognition based on context-free and context-sensitive grammars. We specifically structured a sequence of the driver's eye fixations as well as of the vehicle speed, steering angle and signaling into a sequence of symbolic vectors to form sentences representing a specific driver behavior. It turns out that the proposed approach resulted in an average of 70.5% and 80% recognition rates at the respective 2 and 1 second preceding time to the actual initiation of maneuvering behavior. This performance can be compared to 56.8% and 69% by the state-of-the-art conventional results.

关键词： Vehicles pattern recognition Hidden Markov models Syntactics Accidents Grammar Safety Advanced driver assistance system context-free grammar context-sensitive grammar driver intent eye tracking intelligent transportation system pattern recognition structural pattern recognition syntactic pattern recognition

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Stochastic reasoning for structural pattern recognition: An example from image-based UAV navigation

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pattern recognition 2014年第8期47卷 2732-2744页

作者： Michaelsen, Eckart Meidow, Jochen Fraunhofer IOSB D-76275 Ettlingen Germany

This paper reports on the statistical embedding of a structural pattern recognition system into the autonomous navigation of an unmanned aerial vehicle (UAV). A rule-based system is used for the recognition of visual landmarks such as bridges in aerial views. In principle, rule-based systems can be designed and coded with no training data at hand, but a sound interpretation and utilization of the achieved results needs statistical inference and representative data sets of sufficient coverages. Flying a UAV with an experimental system is expensive, risky, and legally questionable. Therefore, we chose a virtual globe as a camera simulator providing arbitrary amounts of training and test data. The expected positions of landmarks in the aerial views are modeled by mixture models representing inliers, outliers, and intermediate forms which stem from similar structures appearing frequently in the vicinity of landmarks. The parameters of the corresponding likelihood functions are estimated by the Expectation-Maximization method. Using these estimates, we carry out tests and compare the results for heuristic, pessimistic, optimistic, and Bayesian decision rationales. This performance evaluation reveals the superiority of the Bayesian approach. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： structural pattern recognition Decision rationales Expectation-Maximization Navigation UAV

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Bounding Edit Distance for similarity-based sequence classification on structural pattern recognition

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APPLIED SOFT COMPUTING 2020年第PartA期97卷 106778-106778页

作者： Rico-Juan, Juan R. Valero-Mas, Jose J. Inesta, Jose M. Univ Alicante Dept Software & Comp Syst Carretera San Vicente del Raspeig S-N Alicante 03690 Spain

pattern recognition tasks in the structural domain generally exhibit high accuracy results, but their time efficiency is quite low. Furthermore, this low performance is more pronounced when dealing with instance-based classifiers, since, for each query, the entire corpus must be evaluated to find the closest prototype. In this work we address this efficiency issue for the Nearest Neighbor classifier when data are encoded as two-dimensional code sequences, and more precisely strings and sequences of vectors. For this, a set of bounds is proposed in the distance metric that avoid the calculation of unnecessary distances. Results obtained prove the effectiveness of the proposal as it reduces the classification time in percentages between 80% and 90% for string representations and between 60% and 80% for data codified as sequences of vectors with respect to their corresponding non-optimized version of the classifier. (C) 2020 Elsevier B.V. All rights reserved.

关键词： structural pattern recognition Efficient search Classification Nearest neighbor Edit distance

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Inexact graph matching for structural pattern recognition

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pattern recognition LETTERS 1983年第4期1卷 245-253页

作者： Bunke, H. Allermann, G. Univ Erlangen Nurnberg Lehrstuhl Informat Mustererkennung 5 Fr Germany Mannesmann Forsch Inst Duisburg FR Germany

This paper is concerned with the inexact matching of attributed, relational graphs for structural pattern recognition. The matching procedure is based on a state space search utilizing heuristic information. Some expe... 详细信息

关键词： Attributed graphs inexact matching heuristic search structural pattern recognition silhouette recognition

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ENTROPY AND DISTANCE OF RANDOM GRAPHS WITH APPLICATION TO structural pattern-recognition

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 1985年第5期7卷 599-609页

作者： WONG, AKC YOU, M Department of Systems Design Engineering University of Waterloo Waterloo Ont. Canada N2L 3G1 Department of Systems Design Engineering University of Waterloo Waterloo Ont. Canada Mingchi Institute of Technology Taiwan China

The notion of a random graph is formally defined. It deals with both the probabilistic and the structural aspects of relational data. By interpreting an ensemble of attributed graphs as the outcomes of a random graph, we can use its lower order distribution to characterize the ensemble. To reflect the variability of a random graph, Shannon"s entropy measure is used. To synthesize an ensemble of attributed graphs into the distribution of a random graph (or a set of distributions), we propose a distance measure between random graphs based on the minimum change of entropy before and after their merging. When the ensemble contains more than one class of pattern graphs, the synthesis process yields distributions corresponding to various classes. This process corresponds to unsupervised learning in pattern classification. Using the maximum likelihood rule and the probability computed for the pattern graph, based on its matching with the random graph distributions of different classes, we can classify the pattern graph to a class.

关键词： Entropy pattern recognition Design engineering Systems engineering and theory Merging Unsupervised learning pattern classification Distributed computing pattern matching Probability distribution Author Keywords unsupervised classification Attributed graph distance entropy of random graph graph probability distribution random graph structural pattern recognition synthesis of graphs

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structural pattern-recognition OF CAROTID PULSE WAVES USING A GENERAL WAVEFORM PARSING SYSTEM

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COMMUNICATIONS OF THE ACM 1976年第12期19卷 688-695页

作者： STOCKMAN, G KANAL, L KYLE, MC UNIV MARYLAND DEPT COMP SCICOLLEGE PKMD 20742 VET ADM HOSP WASHINGTONDC 20422

Describes a general waveform parsing system with application to structural pattern recognition of carotid pulse waves. Reason for the medical importance of the carotid arterial pulse wave; Application of the syntax-dr... 详细信息

关键词： parsing pusle waves structural pattern recognition waveform analysis

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