A Random Walk Procedure for Texture Discrimination

Wechsler, Harry; Kidode, Masatsugu

doi:10.1109/tpami.1979.4766923

Cited by 42 publications

(18 citation statements)

References 7 publications

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“…For an excellent collection of current knowledge on combinatorial harmonic functions, see [10]. Random walks first appeared in computer vision in the early work of Wechsler and Kidode for texture discrimination [38]. More recently, the average hitting time of a random walk from an object boundary has been studied as a measure to characterize object shape [39].…”

Section: Random Walks and Combinatorial Harmonic Functionsmentioning

confidence: 99%

Random Walks for Image Segmentation

Grady

2006

IEEE Trans. Pattern Anal. Mach. Intell.

2,329

1,871

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Abstract-A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a high-quality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs.

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Section: Random Walks and Combinatorial Harmonic Functionsmentioning

confidence: 99%

Random Walks for Image Segmentation

Grady

2006

IEEE Trans. Pattern Anal. Mach. Intell.

2,329

1,871

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“…A good example of this type of texture is the 112 texture images of the Brodatz album. This album provides a very useful natural texture database, which has been widely used to evaluate texture discrimination methods [30][31][32][33]. Texture from this album can be digitized into different graylevel intervals resulting in different background intensities.…”

Section: Normalized Grayscale Texturesmentioning

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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“…It was firstly introduced to computer vision and image processing for texture discrimination in [24], and later it has been applied to other problems in image processing such as image enhancement [25], image filtering [26], and image segmentation [27]. In geometry processing, it was firstly used for mesh denoising by Sun et al in [28], and then it appears in application of mesh segmentation in [29].…”

Section: B Fcm and Random Walksmentioning

confidence: 99%

3D Shapes Co-segmentation by Combining Fuzzy C-Means with Random Walks

Zhang

Sun

Song

et al. 2013

2013 International Conference on Computer-Aided Design and Computer Graphics

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Co-segmentation of 3D shapes has been receiving increasing attention, and treated as clustering problem in a descriptor space by a few unsupervised approaches to achieve proper co-segmentation of shapes with large variability. However, most of the existing algorithms are performed on segment level and heavily dependent on the per-object segmentation. Accordingly, we propose a co-segmentation method based on combination of Fuzzy C-Means (FCM) and Random Walks together. The novelty of our method is twofold. As an efficient soft clustering algorithm, FCM is firstly used to cluster directly all the facets in the set in terms of their shape descriptors. The clusters of facets are created as candidates of the consistent parts of shapes. Random Walks model is then incorporated into the iterations of FCM clustering to adjust the assignment of facets in each candidate according to the minima rule of shape segmentation. The results of co-segmentation are refined through the iterations of FCM until its convergence conditions are satisfied. Experiments prove that the method proposed in this paper can not only get more stable results without per-object segmentation, but also improve the accuracy of cosegmentation.

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A Random Walk Procedure for Texture Discrimination

Cited by 42 publications

References 7 publications

Random Walks for Image Segmentation

Random Walks for Image Segmentation

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

3D Shapes Co-segmentation by Combining Fuzzy C-Means with Random Walks

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