Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials

Liu, Zhe; Song, Yuqing; Chen, Jianmei; Xie, Chunyan

doi:10.1007/s00521-011-0538-1

Cited by 12 publications

(9 citation statements)

References 41 publications

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“…The mean shift clustering is employed to translate laws into analysis of color layout of an image. In [106] was presented a segmentation method of mixture models of multivariare Chebyshec orthogonal polynomials for color image to solve the problem of over-reliance on a priori assumptions of the parametric methods for finite mixture models and the problem that monic Chebyshev orthogonal polynomials can only process the gray images. The multivariate Chebyshev orthogonal polynomials are derived by the Fourier analysis and tensor product theory, and the nonparametric mixture models of multivariate orthogonal polynomials are proposed.…”

Section: Model-based Methodsmentioning

confidence: 99%

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Segmentation of images by color features: A survey

et al. 2018

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Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.

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Section: Model-based Methodsmentioning

confidence: 99%

“…RGB [8,22,30,51,68,72,94,106,108,119,136,142,146,157,165,170] HSV [30,67,69,100,105,118,137,150,189] HSI [30,67,69,100,105,118,137,150,189] L * a * b * [22,30,66,81,94,139,185] L * u * v * [30,125,155,160,165,191,193] YUV [23,26,27,80,…”

Section: Color Space Referencesmentioning

confidence: 99%

Segmentation of images by color features: A survey

et al. 2018

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“…Greggio et al 130 instituted a fast GMM (FGMM)-based segmentation protocol that automatically inferred the number of components of a GMM as well as their corresponding means and covariances, without necessitating any prior knowledge or conscientious initialization. In contrast to some of the above-described GMM-based approaches, Liu et al 131 advocated the use of nonparametric Fig. 16 Results of the GRF-based segmentation algorithm in Vantaram et al 124 Journal of Electronic Imaging 040901-16 Oct-Dec 2012/Vol.…”

Section: Bayesian Segmentation Techniquesmentioning

confidence: 99%

Survey of contemporary trends in color image segmentation

Vantaram

Saber

2012

J. Electron. Imaging

107

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In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

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“…Previous works have employed several techniques (Aghbarii and Haj, 2006;Carel et al, 2013;Liu et al, 2012;Mignotte, 2010;Mignotte, 2014;Rashedi and Nezamabadi-pour, 2013); but, most of them employ cluster-based methods, particularly Fuzzy C-Means (FCM) (Guo and Sengur, 2013;Huang et al, 2011;Kim, 2014; Mujica-Vargas, Attribution 4.0 International (CC BY 4.0) Share -Adapt Gallegos-Funes and Rosales-Silva, 2013;Nadernejad and Sharifzadeh, 2013;Wang and Dong, 2012). By employing cluster-based methods, groups of colors with similar characteristics are created.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of color images by chromaticity features using self-organizing maps

2016

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Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space is sensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.Keywords: Segmentation of color images, color spaces, competitive neural networks. RESUMENUsualmente, la segmentación de imágenes de color se realiza empleando métodos de agrupamiento y el espacio RGB para representar los colores. El problema con los métodos de agrupamiento es que se requiere conocer previamente la cantidad de grupos, o colores, en la imagen; además de que el espacio RGB es sensible a la intensidad de colores. Los humanos podemos identificar diferentes secciones de una escena solo por la cromaticidad de los colores, ya que representa la característica que nos permite diferenciarlos entre sí. En este artículo se propone emular la percepción humana del color al entrenar un mapa auto-organizado (MAO) con muestras de cromaticidad de diferentes colores. La imagen a procesar es transformada al espacio HSV porque en tal espacio la cromaticidad es separada de la intensidad, mientras que en el espacio RGB no es posible. Nuestra propuesta no requiere conocer previamente la cantidad de colores que hay en una escena, y la iluminación no uniforme no afecta significativamente la segmentación de la imagen. Presentamos resultados experimentales utilizando algunas imágenes de la base de segmentación de Berkeley empleando MAOs de diferentes tamaños, las cuales son segmentadas exitosamente empleando únicamente características de cromaticidad.Palabras clave: Segmentación de imágenes de color, espacios de color, redes neuronales competitivas.

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Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials

Cited by 12 publications

References 41 publications

Segmentation of images by color features: A survey

Segmentation of images by color features: A survey

Survey of contemporary trends in color image segmentation

Segmentation of color images by chromaticity features using self-organizing maps

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