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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.14 n.1 México Jul./Sep. 2010




Is the Coordinated Clusters Representation an analog of the Local Binary Pattern?


¿La Representación de Imágenes por Cúmulos Coordinados es análogo al de Patrones Binarios Locales?


E.V. Kurmyshev


Centro de Investigaciones en Óptica, A.C., Loma del Bosque 115, Lomas del Campestre, 37150 León, Guanajuato, México. E–mail:


Article received on September 22, 2008
Accepted on March 13, 2009



Both the Local Binary Pattern (LBP) and the Coordinated Clusters Representation (CCR) are two methods used successfully in the classification and segmentation of images. They look very similar at first sight. In this work we analyze the principles of the two methods and show that the methods are not reducible to each other. Topologically they are as different as a sphere and a torus. In extracting of image features, the LBP uses a specific technique of binarization of images with the local threshold, defined by the central pixel of a local binary pattern of an image. Then, the central pixel is excluded of each local binary pattern. As a consequence, the mathematical basis of the LBP method is more limited than that of the CCR. In particular, the scanning window of the LBP has always an odd dimensions, while the CCR has no this restriction. The CCR uses a binarization as a preprocessing of images, so that a global or a local threshold can be used for that purpose. We show that a classification based on the CCR of images is potentially more versatile, even though the high performance of both methods was demonstrated in various applications.

Keywords: Texture Image Analysis, Classification, Segmentation, Coordinated Clusters Representation, Local Binary Patterns.



La Representación de Imágenes por Cúmulos Coordinados (RICC) y el Local Binary Pattern (LBP) son métodos eficazmente usados para la clasificación y segmentación de imágenes. A primera vista éstos parecen muy similares. Con un análisis de los principios de dos métodos demostramos que no son reducibles uno a otro; en términos de topología matemática son tan diferentes como esfera y dona. En la etapa de extracción de características de una imagen, el LBP usa una técnica específica de binarización de imágenes con umbral local, que se define por el píxel central de un patrón local de la imagen. Después, el píxel central se excluye de cada patrón local. Por tanto, el sustento matemático del método de LBP es más limitado que el de la RICC. En particular, la ventana de barrido en LBP siempre tiene dimensiones impares, la de la RICC no tiene esta restricción. La RICC requiere la binarización como una etapa de preprocesado de imagen y, por tanto, puede usarse un umbral global o local adaptable. La clasificación basada en la RICC es más versátil, aunque las eficiencias terminales de clasificación por los dos métodos pueden ser muy cercanas en muchas aplicaciones.

Palabras clave: Análisis de Imágenes de Textura, Clasificación, Segmentación, Representación de Imágenes por Cúmulos Coordinados, Patrones Binarios Locales.





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