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VF3-Light: A lightweight subgraph isomorphism algorithm and its experimental evaluation

pattern recognition LETTERS 2019年 125卷 591-596页

作者： Carletti, Vincenzo Foggia, Pasquale Greco, Antonio Vento, Mario Vigilante, Vincenzo Univ Salerno Dept Informat & Elect Engn & Appl Math Fisciano Italy

In this paper we introduce VF3-Light, a simplification of VF3, a recently introduced, general-purpose sub-graph isomorphism algorithm. While VF3 has demonstrated to be very effective on several datasets, especially on very large and very dense graphs, we will show that on some classes of graphs, the full power of VF3 may become an overkill;indeed, by removing some of the heuristics used in it, and as a consequence also some of the data structures that are required by them, we obtain an algorithm (VF3-Light) that is actually faster. In order to provide a characterization of this modified algorithm, we have performed an evaluation using several publicly available graph datasets. Besides comparing VF3-Light with VF3, we have also included in the comparison other recent algorithms that are rated among the fastest in the state of the art. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Graph matching Subgraph isomorphism structural pattern recognition

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Detecting dressing failures using temporal-relational visual grammars

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第7期10卷 2757-2770页

作者： Ruiz, Elias Osmani, Venet Sucar, L. Enrique Mayora, Oscar Inst Nacl Astrofis Opt & Electr Comp Sci Dept Luis Enrique Erro 1 Puebla 72840 Mexico FBK I-38123 Trento TN Italy

Evaluation of dressing activities is essential in the assessment of the performance of patients with psycho-motor impairments. However, the current practice of monitoring dressing activity (performed by the patients in front of the therapist) has a number of disadvantages when considering the personal nature of dressing activity as well as inconsistencies between the recorded performance of the activity and performance of the same activity carried out in the patients' natural environment, such as their home. As such, a system that can evaluate dressing activities automatically and objectively would alleviate some of these issues. However, a number of challenges arise, including difficulties in correctly identifying garments, their position in the body (partially of fully worn) and their position in relation to other garments. To address these challenges, we have developed a novel method based on visual grammars to automatically detect dressing failures and explain the type of failure. Our method is based on the analysis of image sequences of dressing activities and only requires availability of a video recording device. The analysis relies on a novel technique which we call temporal-relational visual grammar;it can reliably recognize temporal dressing failures, while also detecting spatial and relational failures. Our method achieves 91% precision in detecting dressing failures performed by 11 subjects. We explain these results and discuss the challenges encountered during this work.

关键词： Assessing dressing activity Pervasive healthcare Spatial relationships structural pattern recognition Temporal grammars Visual grammars

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Error-tolerant approximate graph matching utilizing node centrality information

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pattern recognition LETTERS 2020年 133卷 313-319页

作者： Dwivedi, Shri Prakash Singh, Ravi Shankar Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

Graph matching is the task of finding the similarity between the two graphs. Error-tolerant graph matching is the process of computing the similarity between the two graphs, where some flexibility to noise or error is allowed to approximate the value of graph matching. In this paper, we present a framework for graph matching by utilizing the centrality measures to ignore the least central nodes of the graphs. Experimental evaluation shows that this approach can be useful to reduce the overall matching time and it can provide time versus accuracy trade-off. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Centrality measures Graph edit distance Graph matching structural pattern recognition

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Prototype generation on structural data using dissimilarity space representation

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NEURAL COMPUTING & APPLICATIONS 2017年第9期28卷 2415-2424页

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

Data reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, prototype selection (PS) and prototype generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: While the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees, or graphs. This work studies the possibility of using dissimilarity space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that the proposed strategy is able to achieve significantly similar results to PS in the initial space, thus standing as a clear alternative to the classic approach, with some additional advantages derived from the DS representation.

关键词： kNN classification Prototype generation structural pattern recognition Dissimilarity space

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Modeling and measuring the spatial relation "along": Regions, contours and fuzzy sets

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pattern recognition 2012年第2期45卷 757-766页

作者： Takemura, Celina Maki Cesar, Roberto M., Jr. Bloch, Isabelle Univ Sao Paulo Inst Math & Stat IME BR-05508090 Sao Paulo Brazil Embrapa Satellite Monitoring Brazilian Agr Res Co BR-13070115 Campinas SP Brazil Telecom ParisTech CNRS LTCI F-75013 Paris France

The analysis of spatial relations among objects in an image is an important vision problem that involves both shape analysis and structural pattern recognition. In this paper, we propose a new approach to characterize the spatial relation along, an important feature of spatial configurations in space that has been overlooked in the literature up to now. We propose a mathematical definition of the degree to which an object A is along an object B, based on the region between A and B and a degree of elongatedness of this region. In order to better fit the perceptual meaning of the relation, distance information is included as well. In order to cover a more wide range of potential applications, both the crisp and fuzzy cases are considered. In the crisp case, the objects are represented in terms of 2D regions or ID contours, and the definition of the alongness between them is derived from a visibility notion and from the region between the objects. However, the computational complexity of this approach leads us to the proposition of a new model to calculate the between region using the convex hull of the contours. On the fuzzy side, the region-based approach is extended. Experimental results obtained using synthetic shapes and brain structures in medical imaging corroborate the proposed model and the derived measures of alongness, thus showing that they agree with the common sense. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Spatial relations Spatial reasoning structural pattern recognition

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Feature selection on node statistics based embedding of graphs

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pattern recognition LETTERS 2012年第15期33卷 1980-1990页

作者： Gibert, Jaume Valveny, Ernest Bunke, Horst Univ Autonoma Barcelona Comp Vis Ctr Bellaterra 08193 Spain Univ Bern Inst Comp Sci & Appl Math CH-3012 Bern Switzerland

Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graph embedding. A key issue in graph embedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods. (c) 2012 Elsevier B.V. All rights reserved.

关键词： structural pattern recognition Graph embedding Feature ranking PCA Graph classification

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Probabilistic finite-state machines - Part I

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2005年第7期27卷 1013-1025页

作者： Vidal, E Thollard, F de la Higuera, C Casacuberta, F Carrasco, RC Univ Politecn Valencia Dept Sistemas Informat & Computac E-46071 Valencia Spain Univ Politecn Valencia Inst Informat Technol E-46071 Valencia Spain EURISE Fac Sci & Tech FR-42023 St Etienne France Univ Alicante Dept Lenguajes & Sistemas Informat E-03071 Alicante Spain

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 definitions and properties. In Part II, we will study the relation of probabilistic finite- state automata with other well- known devices that generate strings as hidden Markov models and n- grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.

关键词： automata classes defined by grammars or automata machine learning language acquisition language models language parsing and understanding machine translation speech recognition and synthesis structural pattern recognition syntactic pattern recognition

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A binary linear programming formulation of the graph edit distance

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2006年第8期28卷 1200-1214页

作者： Justice, Derek Hero, Alfred Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem. A general formulation for editing graphs is used to derive a graph edit distance that is proven to be a metric, provided the cost function for individual edit operations is a metric. Then, a binary linear program is developed for computing this graph edit distance, and polynomial time methods for determining upper and lower bounds on the solution of the binary program are derived by applying solution methods for standard linear programming and the assignment problem. A recognition problem of comparing a sample input graph to a database of known prototype graphs in the context of a chemical information system is presented as an application of the new method. The costs associated with various edit operations are chosen by using a minimum normalized variance criterion applied to pairwise distances between nearest neighbors in the database of prototypes. The new metric is shown to perform quite well in comparison to existing metrics when applied to a database of chemical graphs.

关键词： graph algorithms similarity measures structural pattern recognition graphs and networks linear programming continuation (homotopy) methods

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On the detection of tracks in spectrogram images

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pattern recognition 2013年第5期46卷 1396-1408页

作者： Lampert, Thomas A. O'Keefe, Simon E. M. Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

This paper proposes an active contour algorithm for spectrogram track detection. It extends upon previously published work in a number of areas, previously published internal and potential energy models are refined and theoretical motivations for these changes are offered. These refinements offer a marked improvement in detection performance, including a notable reduction in the probability of false positive detections. The result is feature extraction at signal-to-noise ratios as low as -1 dB in the frequency domain. These theoretical and experimental findings are related to existing solutions to the problem, offering a new insight into their limitations. We show, through complexity analysis, that this is achievable in real-time. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Active contour Energy minimisation Remote sensing Spectrogram Statistical pattern recognition structural pattern recognition

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Error-tolerant graph matching using node contraction

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pattern recognition LETTERS 2018年 116卷 58-64页

作者： Dwivedi, Shri Prakash Singh, Ravi Shankar Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

Graph matching is the task of computing the similarity between two graphs. It is widely used in graph-based structural pattern recognition. Error-tolerant graph matching is a type of graph matching, in which a similarity between two graphs is computed based on some tolerance value whereas in exact graph matching a strict one-to-one correspondence is required between two graphs. In this paper, we present an approach to error-tolerant graph matching using node contraction where the given graph is transformed into another graph by contracting smaller degree nodes. We use this scheme to extend the notion of graph edit distance, which can be used as a trade-off between execution time and accuracy requirements of various graph matching applications. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Graph matching Graph edit distance structural pattern recognition

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