Image segmentation based on the integration of colour–texture descriptors—A review

Ilea, Dana E.; Whelan, Paul F.

doi:10.1016/j.patcog.2011.03.005

Cited by 180 publications

(81 citation statements)

References 125 publications

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“…We selected it because it naturally handles multi-region unsupervised segmentation and avoids degenerating when a method greatly over-or under-segments an image. There are a wide variety of other, similarly good performance metrics for segmentation algorithms; see Section 3 of [46] for a good overview. In addition to the Rand index, we provide results in terms of the variation of information [47] on the reproducible research page for this paper; the ranking of methods is largely unchanged between metrics and the best-performing method for each dataset does not change.…”

Section: Methodsmentioning

confidence: 99%

Images as Occlusions of Textures: A Framework for Segmentation

McCann

Mixon

Fickus

et al. 2014

IEEE Trans. on Image Process.

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Abstract-We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on non-negative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.

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Section: Methodsmentioning

confidence: 99%

Images as Occlusions of Textures: A Framework for Segmentation

McCann

Mixon

Fickus

et al. 2014

IEEE Trans. on Image Process.

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“…For the estimation of these measures, we have used the MATLAB® source code made publicly available by Yang et al 35 Other performance measures for segmentation algorithms may be consulted in Table 1 of the article by Ilea and Whelan. 3 The BDE measures the average displacement error between the boundary pixels of the segmented image and the closest boundary pixels in the ground-truth segmentations. The GCE evaluates the extent to which a segmentation map can be considered as a refinement of another segmentation.…”

Section: Experimental Setup and Rct Parametermentioning

confidence: 99%

“…In particular, the integration of color and texture cues is strongly related to the human perception. 3 Recently, Ilea and Whelan 3 have categorized the segmentation methods according to the approach used for the extraction and integration of color and texture features. Three major trends have been identified: (1) implicit color-texture integration, where the texture is extracted from one or multiple color channels, (2) extraction of features in succession and, (3) extraction of color and texture features on separate channels and their combination in the segmentation process.…”

Section: Introductionmentioning

confidence: 99%

Integration of color and texture cues in a rough set–based segmentation method

Lizarraga-Morales

Sánchez-Yáñez

Ayala-Ramírez

et al. 2014

J. Electron. Imaging

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Abstract. We propose the integration of color and texture cues as an improvement of a rough set-based segmentation approach, previously implemented using only color features. Whereas other methods ignore the information of neighboring pixels, the rough set-based approximations associate pixels locally. Additionally, our method takes into account pixel similarity in both color and texture features. Moreover, our approach does not require cluster initialization because the number of segments is determined automatically. The color cues correspond to the a and b channels of the CIELab color space. The texture features are computed using a standard deviation map. Experiments show that the synergistic integration of features in this framework results in better segmentation outcomes, in comparison with those obtained by other related and state-of-the-art methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Recently, we have seen a growing interest in color texture [24]. This is a natural evolution of the field of texture, from grayscale to color texture.…”

Section: Introductionmentioning

confidence: 99%

“…The use of color in texture analysis showed several benefits [25][26][27]. In color texture, efforts have been made to find efficient methods to combine color and texture features [24]. Consequently, the evaluation of color texture methods requires images in which color and texture information are both sources of discriminative information.…”

Section: Introductionmentioning

confidence: 99%

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

Safia

2013

ISRN Machine Vision

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Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

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Image segmentation based on the integration of colour–texture descriptors—A review

Cited by 180 publications

References 125 publications

Images as Occlusions of Textures: A Framework for Segmentation

Images as Occlusions of Textures: A Framework for Segmentation

Integration of color and texture cues in a rough set–based segmentation method

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

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