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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 7 • Date July 2005

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Displaying Results 1 - 20 of 20
  • [Front cover]

    Publication Year: 2005 , Page(s): c1
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  • [Inside front cover]

    Publication Year: 2005 , Page(s): c2
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  • Guest editors' introduction to the special section on syntactic and structural pattern recognition

    Publication Year: 2005 , Page(s): 1009 - 1012
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (99 KB) |  | HTML iconHTML  

    This paper presents the guest editors' introduction to the special section which was planned in honor of the memory of the late Professor King-Sun Fu. Dr. King-Sun Fu is widely recognized for his paramount contributions in the field of pattern recognition, especially in the area of syntactic and structural pattern recognition. The paper discusses the problems of interest regarding syntactic and st... View full abstract»

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  • Probabilistic finite-state machines - part I

    Publication Year: 2005 , Page(s): 1013 - 1025
    Cited by:  Papers (41)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (316 KB) |  | HTML iconHTML  

    Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their defin... View full abstract»

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  • Probabilistic finite-state machines - part II

    Publication Year: 2005 , Page(s): 1026 - 1039
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (387 KB) |  | HTML iconHTML  

    Probabilistic finite-state machines are used today in a variety of areas in pattern recognition or in fields to which pattern recognition is linked. In part I of this paper, we surveyed these objects and studied their properties. In this part, we study the relations between probabilistic finite-state automata and other well-known devices that generate strings like hidden Markov models and n-grams ... View full abstract»

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  • Parsing with probabilistic strictly locally testable tree languages

    Publication Year: 2005 , Page(s): 1040 - 1050
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (491 KB) |  | HTML iconHTML  

    Probabilistic k-testable models (usually known as k-gram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events during pattern classification. In this paper, we introduce the family of stochastic k-testable tree languages and describe how these models can approximate any stochastic rational tree language. The model is appl... View full abstract»

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  • Grammatical inference in bioinformatics

    Publication Year: 2005 , Page(s): 1051 - 1062
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (972 KB) |  | HTML iconHTML  

    Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden... View full abstract»

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  • Learning deterministic finite automata with a smart state labeling evolutionary algorithm

    Publication Year: 2005 , Page(s): 1063 - 1074
    Cited by:  Papers (7)  |  Patents (8)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (751 KB) |  | HTML iconHTML  

    Learning a deterministic finite automaton (DFA) from a training set of labeled strings is a hard task that has been much studied within the machine learning community. It is equivalent to learning a regular language by example and has applications in language modeling. In this paper, we describe a novel evolutionary method for learning DFA that evolves only the transition matrix and uses a simple ... View full abstract»

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  • Structural semantic interconnections: a knowledge-based approach to word sense disambiguation

    Publication Year: 2005 , Page(s): 1075 - 1086
    Cited by:  Papers (51)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (883 KB) |  | HTML iconHTML  

    Word sense disambiguation (WSD) is traditionally considered an AI-hard problem. A break-through in this field would have a significant impact on many relevant Web-based applications, such as Web information retrieval, improved access to Web services, information extraction, etc. Early approaches to WSD, based on knowledge representation techniques, have been replaced in the past few years by more ... View full abstract»

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  • Polynomial-time metrics for attributed trees

    Publication Year: 2005 , Page(s): 1087 - 1099
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1074 KB) |  | HTML iconHTML  

    We address the problem of comparing attributed trees and propose four novel distance measures centered around the notion of a maximal similarity common subtree. The proposed measures are general and defined on trees endowed with either symbolic or continuous-valued attributes and can be applied to rooted as well as unrooted trees. We prove that our measures satisfy the metric constraints and provi... View full abstract»

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  • Exact and approximate graph matching using random walks

    Publication Year: 2005 , Page(s): 1100 - 1111
    Cited by:  Papers (32)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1082 KB) |  | HTML iconHTML  

    In this paper, we propose a general framework for graph matching which is suitable for different problems of pattern recognition. The pattern representation we assume is at the same time highly structured, like for classic syntactic and structural approaches, and of subsymbolic nature with real-valued features, like for connectionist and statistic approaches. We show that random walk based models,... View full abstract»

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  • Pattern vectors from algebraic graph theory

    Publication Year: 2005 , Page(s): 1112 - 1124
    Cited by:  Papers (50)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (878 KB) |  | HTML iconHTML  

    Graph structures have proven computationally cumbersome for pattern analysis. The reason for this is that, before graphs can be converted to pattern vectors, correspondences must be established between the nodes of structures which are potentially of different size. To overcome this problem, in this paper, we turn to the spectral decomposition of the Laplacian matrix. We show how the elements of t... View full abstract»

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  • Indexing hierarchical structures using graph spectra

    Publication Year: 2005 , Page(s): 1125 - 1140
    Cited by:  Papers (35)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1053 KB) |  | HTML iconHTML  

    Hierarchical image structures are abundant in computer vision and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic graph (DAG) into a low-dimensional vector space. Based on a novel spectral characterization of a DAG, t... View full abstract»

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  • Generic model abstraction from examples

    Publication Year: 2005 , Page(s): 1141 - 1156
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (2788 KB) |  | HTML iconHTML  

    The recognition community has typically avoided bridging the representational gap between traditional, low-level image features and generic models. Instead, the gap has been artificially eliminated by either bringing the image closer to the models using simple scenes containing idealized, textureless objects or by bringing the models closer to the images using 3D CAD model templates or 2D appearan... View full abstract»

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  • A probabilistic model of face mapping with local transformations and its application to person recognition

    Publication Year: 2005 , Page(s): 1157 - 1171
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (603 KB) |  | HTML iconHTML  

    This paper proposes a new measure of "distance" between faces. This measure involves the estimation of the set of possible transformations between face images of the same person. The global transformation, which is assumed to be too complex for direct modeling, is approximated by a patchwork of local transformations, under a constraint imposing consistency between neighboring local transformations... View full abstract»

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  • A trained spin-glass model for grouping of image primitives

    Publication Year: 2005 , Page(s): 1172 - 1182
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (1327 KB) |  | HTML iconHTML  

    A method is presented that uses grouping to improve local classification of image primitives. The grouping process is based upon a spin-glass system, where the image primitives are treated as possessing a spin. The system is subject to an energy functional consisting of a local and a bilocal part, allowing interaction between the image primitives. Instead of defining the state of lowest energy as ... View full abstract»

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  • Call For Papers

    Publication Year: 2005 , Page(s): 1183
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  • [Advertisement]

    Publication Year: 2005 , Page(s): 1184
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  • [Inside back cover]

    Publication Year: 2005 , Page(s): c3
    Save to Project icon | Request Permissions | PDF file iconPDF (76 KB)  
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  • [Back cover]

    Publication Year: 2005 , Page(s): c4
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Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
David A. Forsyth
University of Illinois