Color image segmentation using an enhanced Gradient Network Method

Wangenheim, Aldo von; Bertoldi, Rafael F.; Abdala, Daniel Duarte; Sobieranski, Antonio Carlos; Coser, Leandro; Jiang, Xiaoyi; Richter, Michael M.; Priese, Lutz; Schmitt, Francis

doi:10.1016/j.patrec.2009.07.005

Cited by 13 publications

(12 citation statements)

References 24 publications

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“…In order to reduce the subjectivity during the comparison of the results against the GTs, we employed the Rand index 27 as an objective automated comparison method, employing a strategy consistent with previous work. 3,5,8 The Rand index is a comparison score, ranging from 0 to 1, between an image and a GT, where the closer to 0 the returned value is, the more similar to the reference segmentation is the result. The choice of Rand as metric for the analysis of the results was based on past experiments 7 with other GT analysis methods, where the results obtained using Rand were more similar to human analysis than Fowlkes and Mallows, 28 Ben-Hur et al 29 or Dongen.…”

Section: Resultsmentioning

confidence: 99%

“…Graph-based image segmentation methods, [1][2][3][4] where a graph of image segments is traversed in order to join similar segments, have been used in the last decades for low level image processing tasks. Even if some of these approaches employ additional discriminating heuristics when merging regions, 3 the core color discriminating function employed has still been a linear metric defined in some color space such as RGB, HSV/HSI or CIELab.…”

Section: Introductionmentioning

confidence: 99%

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Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics

Carvalho

Neto

Sobieranski

et al. 2015

Int. J. Image Grap.

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We present a new segmentation method called weighted Felzenszwalb and Huttenlocher (WFH), an improved version of the well-known graph-based segmentation method, Felzenszwalb and Huttenlocher (FH). Our algorithm uses a nonlinear discrimination function based on polynomial Mahalanobis Distance (PMD) as the color similarity metric. Two empirical validation experiments were performed using as a golden standard ground truths (GTs) from a publicly available source, the Berkeley dataset, and an objective segmentation quality measure, the Rand dissimilarity index. In the first experiment the results were compared against the original FH method. In the second, WFH was compared against several well-known segmentation methods. In both case,s WFH presented significant better similarity results when compared with the golden standard and segmentation results presented a reduction of over-segmented regions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics

Carvalho

Neto

Sobieranski

et al. 2015

Int. J. Image Grap.

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“…From the analysis of the segmentation approaches, it was chosen a set of specific implementation which we understood, is widely accepted and represents properly each particular segmentation philosophy: Color Structure Code (CSC) [7,8,9], Mumford-Shah (MS) [10,11] and Felzenszwalb and Huttenlocher (FH) [12] representing respectively split and merge, variational region growing and Graph-Based Segmentation. As nonlinear color similarity metric we employed the Poly-nomial Mahalanobis Distance (PMD) [13] in the RGB space [14].…”

Section: Methodsmentioning

confidence: 99%

Can the Use of nonlinear Color Metrics systematically improve Segmentation?

Carvalho

Neto

Comunello

et al. 2018

RITA

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Image segmentation is a procedure where an image is split into its constituent parts, according to some criterion. In the literature, there are different well-known approaches for segmentation, such as clustering, thresholding, graph theory and region growing. Such approaches, additionally, can be combined with color distance metrics, playing an important role for color similarity computation. Aiming to investigate general approaches able to enhance the performance of segmentation methods, this work presents an empirical study of the effect of a nonlinear color metric on segmentation procedures. For this purpose, three algorithms were chosen: Mumford-Shah, Color Structure Code and Felzenszwalb and Huttenlocher Segmentation. The color similarity metric employed by these algorithms (L2-norm) was replaced by the Polynomial Mahalanobis Distance. This metric is an extension of the statistical Mahalanobis Distance used to measure the distance between coordinates and distribution centers. An evaluation based upon automated comparison of segmentation results against ground truths from the Berkeley Dataset was performed. All three segmentation approaches were compared to their traditional implementations, against each other and also to a large set of other segmentation methods. The statistical analysis performed has indicated a systematic improvement of segmentation results for all three segmentation approaches when the nonlinear metric was employed.

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“…Apart from the procedures discussed in this subsection, numerous supervised/unsupervised segmentation methods involving graph-cuts [178][179][180][181] and hybrid techniques using graph formulations [182][183][184][185][186][187][188] have been developed as tools for driving various imaging applications.…”

Section: Journal Of Electronic Imaging 040901-18mentioning

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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Color image segmentation using an enhanced Gradient Network Method

Cited by 13 publications

References 24 publications

Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics

Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics

Can the Use of nonlinear Color Metrics systematically improve Segmentation?

Survey of contemporary trends in color image segmentation

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