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Learning spatial relationships in hand-drawn patterns using fuzzy mathematical morphology

Learning spatial relationships in hand-drawn patterns using ...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Adrien Delaye Eric Anquetil INSA Rennes Rennes France UMR IRISA Rennes France Universite Europeenne de Bretagne France

We introduce in this work a new approach for learning spatial relationships between elements of hand-drawn patterns with the help of fuzzy mathematical morphology operators. Relying on mathematical morphology allows to take into account the actual shapes of hand-drawn patterns when modeling their spatial relationships, and thus to cope with the variability of handwriting signal. Extension of mathematical morphology to the fuzzy set framework further allows to handle imprecision of handwriting and to deal with the ambiguity of spatial relationships. The novelty lies in the generative aspect of the models we propose, in the sense that they can exhibit the region of space where the learnt relation is satisfied with respect to a reference object, and can thus be used for driving structural analysis of complex patterns. Experiments over on-line handwritten data show their performance, and prove their ability to deal with variability of handwriting and reasoning under imprecision.

关键词： Mathematical model Computational modeling Shape Morphology pattern recognition Predictive models Adaptation model

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SIMCOMP: A Hybrid soft Clustering of Metagenome Reads

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5th international conference on pattern recognition in Bioinformatics

作者： Prabhakara, Shruthi Acharya, Raj Penn State Univ Dept Comp Sci & Engn State Coll PA 16801 USA

ISBN: (纸本)9783642160004

A major challenge facing metagenomics is the development of tools for the characterization of functional and taxonomic content of vast amounts of short metagenome reads. In this paper, we present a two pass semi-supervised algorithm, SimComp, for soft clustering of short metagenome reads, that is a hybrid of comparative and composition based methods. In the first pass, a comparative analysis of the metagenome reads against BLASTx extracts the reference sequences from within the metagenome to form an initial set of seeded clusters. Those reads that have a significant match to the database are clustered by their phylogenetic provenance. In the second pass, the remaining fraction of reads are characterized by their species-specific composition based characteristics. SimComp groups the reads into overlapping clusters, each with its read leader. We make no assumptions about the taxonomic distribution of the dataset. The overlap between the clusters elegantly handles the challenges posed by the nature of the metagenomic data. The resulting cluster leaders can be used as an accurate estimate of the phylogenetic composition of the metagenomic dataset. Our method enriches the dataset into a small number of clusters, while accurately assigning fragments as small as 100 base pairs.

关键词： Phylogenetic

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Classification of brand names based on n-grams

Classification of brand names based on n-grams

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international conference of soft computing and pattern recognition (SOCPAR)

作者： P. Warintarawej A. Laurent P. Pompidor B. Laurent CNRS LIRMM Universite Montpellier 2 Montpellier France Namae Concept Montpellier France

Supervised classification has been extensively addressed in the literature as it has many applications, especially for text categorization or web content mining where data are organized through a hierarchy. On the other hand, the automatic analysis of brand names can be viewed as a special case of text management, although such names are very different from classical data. They are indeed often neologisms, and cannot be easily managed by existing NLP tools. In our framework, we aim at automatically analyzing such names and at determining to which extent they are related to some concepts that are hierarchically organized. The system is based on the use of character n-grams. The targeted system is meant to help, for instance, to automatically determine whether a name sounds like being related to ecology.

关键词： Accuracy Thesauri Training Buildings Companies pattern recognition Training data

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TIGER: Querying large tables through criteria extension

TIGER: Querying large tables through criteria extension

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Yoann Pitarch Dominique Laurent Pascal Poncelet Nicolas Spyratos LIRMM-CNRS University of Montpellier 2 Montpellier France ETIS-CNRS Cergy-Pontoise University Cergy-Pontoise France LRI-CNRS Universite Paris 11 Orsay France

Sales on the Internet have increased significantly during the last decade, and so, it is crucial for companies to retain customers on their web site. Among all strategies towards this goal, providing customers with a flexible search tool is a crucial issue. In this paper, we propose an approach, called TIGER, for handling such flexibility automatically. More precisely, if the search criteria of a given query to a relational table or a Web catalog are too restrictive, our approach computes a new query combining extensions of the criteria. This new query maximizes the quality of the answer, while being as close as possible to the original query. Experiments show that our approach improves the quality of queries, in the sense explained just above.

关键词： Color Context Web sites pattern recognition Marketing and sales Companies Semantics

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Concentric Circular Antenna Array synthesis using a differential Invasive Weed Optimization algorithm

Concentric Circular Antenna Array synthesis using a differen...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Siddharth Pal Aniruddha Basak Swagatam Das Ajith Abraham Ivan Zelinka Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Machine Intelligence Research Laboratories USA FEI-VSB-TU Ostrava Czech Republic

Concentric Circular Antenna Array (CCAA) has several interesting features that makes it indispensable in mobile and communication applications. Here we have considered a uniform arrangement of elements where the inter-element spacing is kept half a wavelength. The main aim is to reduce the sidelobe levels and the primary lobe beamwidth as much as possible. Central to our design is a hybridization of two prominent metaheuristics of current interest namely the Invasive Weed Optimization (IWO) and Differential Evolution (DE). The results of the DIWO algorithm have been shown to perform better then other state-of-the-art metaheuristics like the Particle Swarm Optimization (PSO), DE and IWO.

关键词： Arrays Antenna arrays Algorithm design and analysis Optimization Antenna radiation patterns pattern recognition Indexes

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Partial approximative set theory: A generalization of the rough set theory

Partial approximative set theory: A generalization of the ro...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Zoltán Csajbók Department of Health Informatics Faculty of Health University of Debrecen Nyíregyháza Hungary

There are close links between mathematical morphology and rough set theory. Both theories are successfully applied among others to image processing and pattern recognition. This paper presents a new generalization of the classical rough set theory, called the partial approximative set theory (PAST). According to Pawlak's classic rough set theory, the vagueness of a subset of a finite universe is defined by the difference of its upper and lower approximations with respect to an equivalence relation on the universe. There are two most natural ways of the generalization of this idea. Namely, the equivalence relation is replaced by either any other type of binary relations on the universe or an arbitrary covering of the universe. In this paper, our starting point will be an arbitrary family of subsets of an arbitrary universe, neither that it covers the universe nor that the universe is finite will be assumed. We will give some reasons why this new approach is worth studying, and put our discussions into an overall treatment, called the general approximation framework.

关键词： Approximation methods pattern recognition Rough sets Morphology Image processing Mathematical model

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A hybrid image representation for indoor scene classification

A hybrid image representation for indoor scene classificatio...

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25th international conference of Image and Vision computing New Zealand, IVCNZ 2010

作者： Niu, Zhibin Zhou, Yue Shi, Kun Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9781424496303

Although scene classification has been studied for decades, indoor scene recognition remains challenging due to its large view point variance and massive irregular artefacts. In fact, most existing methods for outdoor scene classification perform poorly in the indoor situation. To address the problem, we propose a hybrid image representation by combining the global information with the local structure of the scene. First, the global discriminative information is captured by pyramid GIST feature. Second, the local structure is encoded by the bag of features method with Histogram Intersection Kernel (HIK). Finally, HIK based SVM is employed for learning and classification. Experiments on the MIT indoor scene database show that our approach could significantly improve the recognition accuracy of the state-of-art methods by about 14%. © 2010 IEEE.

关键词： Image representation

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A New Approach for Vein pattern-Based recognition

A New Approach for Vein Pattern-Based Recognition

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6th international conference on Intelligent computing

作者： Soni, Mohit Gupta, Sandesh Gupta, Phalguni Directorate Forens Sci CGO Complex New Delhi 110003 India Indian Inst Technol Kanpur 208014 Uttar Pradesh India

ISBN: (纸本)9783642149214

This paper presents an efficient human verification system based on vein patterns of the hand A new absorption based technique his been proposed to collect good quality images with the help of a low cost camera and light source The system automatically detects the region of interest from the image and does the necessary processing to extract vinous features Matching technique based on Euclidean Distance has been used for making the decision It has been tested on a data set of 1750 image samples collected from 341 individuals The accuracy of the recognition system is found to be 99 26% with FRR of 0 03%

关键词： recognition system Palma Dorsa Region of Interest Vein Structure Minutiae Ridge Forkings

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Harmony search algorithm for simultaneous clustering and feature selection

Harmony search algorithm for simultaneous clustering and fea...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Hadi Sarvari Nima Khairdoost Abdolvahhab Fetanat Department of computer Faculty engineering Islamic Azad University Behbahan Iran

Feature selection is an important process in data analysis for information-preserving data reduction. Clustering is inherently a difficult task and is made even more difficult when the selection of relevant features is also an issue. In this paper, we propose an approach for clustering and feature selection simultaneously using a harmony search algorithm. Our approach makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both feature selection and clustering, without making any a prior assumption about the number of clusters. Within ourmethod, a variable composite representation is devised toencode both feature selection and cluster centers with different numbers of clusters. Furthermore, local search operations are used to improve feature selection and cluster centers encoded in the harmonics.

关键词： Clustering algorithms Algorithm design and analysis Computers pattern recognition Indexes Scattering Search problems

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A self-growing Bayesian network classifier for online learning of human motion patterns

A self-growing Bayesian network classifier for online learni...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Zhuo Chen Nelson H. C. Yung Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong China

This paper proposes a new self-growing Bayesian network classifier for online learning of human motion patterns (HMPs) in dynamically changing environments. The proposed classifier is designed to represent HMP classes based on a set of historical trajectories labeled by unsupervised clustering. It then assigns HMP class labels to current trajectories. Parameters of the proposed classifier are recalculated based on the augmented dataset of labeled trajectories and all HMP classes are accordingly updated. As such, the proposed classifier allows current trajectories to form new HMP classes when they are sufficiently different from existing HMP classes. The performance of the proposed classifier is evaluated by a set of real-world data. The results show that the proposed classifier effectively learns new HMP classes from current trajectories in an online manner.

关键词： Trajectory Bayesian methods Humans Support vector machine classification pattern recognition Testing Accuracy

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