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JournalISSN: 0218-0014

International Journal of Pattern Recognition and Artificial Intelligence 

World Scientific
About: International Journal of Pattern Recognition and Artificial Intelligence is an academic journal published by World Scientific. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 0218-0014. Over the lifetime, 3119 publications have been published receiving 46512 citations.


Papers
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Journal ArticleDOI
TL;DR: This paper will try to characterize the role that graphs play within the Pattern Recognition field, and presents two taxonomies that include almost all the graph matching algorithms proposed from the late seventies and describes the different classes of algorithms.
Abstract: A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field.

1,517 citations

Journal ArticleDOI
TL;DR: In this article, a Siamese time delay neural network is used to measure the similarity between pairs of signatures, and the output of this half network is the feature vector for the input signature.
Abstract: This paper describes the development of an algorithm for verification of signatures written on a touch-sensitive pad. The signature verification algorithm is based on an artificial neural network. The novel network presented here, called a “Siamese” time delay neural network, consists of two identical networks joined at their output. During training the network learns to measure the similarity between pairs of signatures. When used for verification, only one half of the Siamese network is evaluated. The output of this half network is the feature vector for the input signature. Verification consists of comparing this feature vector with a stored feature vector for the signer. Signatures closer than a chosen threshold to this stored representation are accepted, all other signatures are rejected as forgeries. System performance is illustrated with experiments performed in the laboratory.

1,297 citations

Journal ArticleDOI
TL;DR: This paper provides a review of the classification of imbalanced data regarding the application domains, the nature of the problem, the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.
Abstract: Classification of data with imbalanced class distribution has encountered a significant drawback of the performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. This paper provides a review of the classification of imbalanced data regarding: the application domains; the nature of the problem; the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.

1,268 citations

Journal ArticleDOI
TL;DR: A tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks is provided, and a discussion of Bayesian methods for model selection in generalized HMMs is discussed.
Abstract: We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective make sit possible to consider novel generalizations to hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.

760 citations

Journal ArticleDOI
TL;DR: The main features of so-called wavelet transforms are illustrated through simple mathematical examples and the first applications of the method to the recognition and visualisation of characteristic features of speech and of musical sounds are presented.
Abstract: This paper starts with a brief discussion of so-called wavelet transforms, i.e., decompositions of arbitrary signals into localized contributions labelled by a scale parameter. The main features of the method are first illustrated through simple mathematical examples. Then we present the first applications of the method to the recognition and visualisation of characteristic features of speech and of musical sounds.

622 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023105
2022266
2021178
2020208
2019190
2018141