Complex networks : application for texture characterization and classification

Chalumeau, T.; Costa, Luciano da F.; Laligant, Olivier; Mériaudeau, Fabrice

doi:10.5565/rev/elcvia.247

Cited by 9 publications

(14 citation statements)

References 14 publications

(12 reference statements)

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“…Increment threshold t f Final threshold ϕ Feature vector of the proposed method been modeled as complex networks [6,14,15,21,48,51] where the Complex Networks Theory is used to represent and characterize the relation among structural elements of texture. The reader may consult [46,65,67] for a review of texture methods.…”

Section: Symbolmentioning

confidence: 99%

Texture recognition based on diffusion in networks

Gonçalves

Silva

Costa

et al. 2016

Information Sciences

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Section: Symbolmentioning

confidence: 99%

Texture recognition based on diffusion in networks

Gonçalves

Silva

Costa

et al. 2016

Information Sciences

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“…In the fact, every discrete structure such as lists, trees, networks, texts [1] and images [9] can be suitably represented as graphs. Taking this into account, various studies include investigations of the problem representation as a Complex Network, followed by an analysis of its topological characteristics and its features extraction [6,4,3,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Thoses measurements are used to classify shapes in differents classes. In [6,7] and [8], the problem of texture characterization is presented in terms of complex networks: image pixels are represented as nodes and similarities between such pixels are mapped as links between the network nodes. It is verified that several types of textures present distinct node degree distributions, suggesting complex organization of those textures.…”

Section: Introductionmentioning

confidence: 99%

A Complex Network-Based Approach for Texture Analysis

Backes

Casanova²,

Bruno³

2010

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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In this paper, we propose a novel texture analysis method using the complex network theory. It was investigated how a texture image can be effectively represented, characterized and analyzed in terms of a complex network. The propose uses degree measurements in a dynamic evolution network to compose a set of feasible shape descriptors. Results show that the method is very robust and it presents a very excellent texture discrimination for all considered classes.

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“…Em Chalumeau et al (8)(9)(10), o peso da arestaé uma relação entre a distância entre os pixeis e a diferença de intensidade de cor. Arestas abaixo de um determinado limiar são retiradas do grafo inicial.…”

Section: Extração De Características De Texturaunclassified

“…Um modelo similar ao apresentado acima, desenvolvido de maneira independente e simultânea, foi proposto por Chalumeau et al (8). O autor utiliza um modelo PAG lattice r-conectada, porém só considera a diferença de intensidade do nível de cinza como peso da aresta e utiliza apenas o grau médio e coeficiente de aglomeração como características.…”

Section: Pag Lattice R-conectadaunclassified

Redes complexas em visão computacional com aplicações em bioinformática

Casanova¹

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AGRADECIMENTOSAos meus pais Darci e Acioní, que mesmoà distância, me apoiam e me encorajaram em todos os momentos.A minha amada namorada Larissa, pela felicidade a mim proporcionada, pela compreensão, carinho e companhia oferecida durante o tempo em que me mantive distante.Ao meu orientador Prof. Dr. Odemir Martinez Bruno, pela confiança depositada em mim, orientação e amizade.Aos amigos que fiz no convívio diário da universidade, e aos amigos do ciclismo, que me proporcionaram viagens, diversão e experiênciasúnicas.Aos professores e funcionários do IFSC -USP e a todos que, direta ou indiretamente, colaboraram comigo.A FAPESP pelo apoio financeiro.Eu não tenhoídolos. Tenho admiração por trabalho, dedicação e competência. Complex networks is a relatively recent field of study, that has called the attention of the scientific community and has been successfully applied in different areas such as computer networking, sociology, medicine, physics, mathematics and others. However the literature shows that there are few works that employ complex networks in feature extraction of images for later analysis or classification. Given an image, it can be modeled as a network, extract topological features and, using these measures, build the classifier desired. This work aims, therefore, investigate this type of application, analyzing new forms of modeling an image as a complex network and investigate some topological features to characterize images. In order to analyze the potential of the techniques developed, we selected a major challenge in the field of computer vision: plant identification by leaf analysis. The plant identification is an important task in many research fields such as biodiversity, ecology, botany, pharmacology and others.

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Complex networks : application for texture characterization and classification

Cited by 9 publications

References 14 publications

Texture recognition based on diffusion in networks

Texture recognition based on diffusion in networks

A Complex Network-Based Approach for Texture Analysis

Redes complexas em visão computacional com aplicações em bioinformática

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