A Comparative Study of Texture Measures for Terrain Classification

Weszka, Joan S.; Dyer, Charles R.; Rosenfeld, Azriel

doi:10.1109/tsmc.1976.5408777

Cited by 1,301 publications

(534 citation statements)

References 2 publications

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“…8, minimum and total variations are not su cient to discriminate between all textures [42][43][44] (as they are very similar to the contrast [45] and the gray level di erence statistics [46]), but it is recognized that in general, di erent operators correspond to di erent images. In this work, speciÿc knowledge of the acquisition process is not applied and we try to demonstrate the validity of our neural implementation to a greater extent than the operator's one.…”

Section: Comparisons With Other Segmentation Methodsmentioning

confidence: 99%

A clustering fuzzy approach for image segmentation

Cinque

Foresti

Lombardi³

2004

Pattern Recognition

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Segmentation is a fundamental step in image description or classiÿcation. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of ine ciency. This paper presents a clustering approach for image segmentation based on a modiÿed fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to ÿnd a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations conÿrm the validity of the approach proposed.

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Section: Comparisons With Other Segmentation Methodsmentioning

confidence: 99%

A clustering fuzzy approach for image segmentation

Cinque

Foresti

Lombardi³

2004

Pattern Recognition

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“…In this paper, a texture information comparison function is proposed by comparing the correlation texture information and autocorrelation texture information between different neighborhoods. The correlation texture information is defined as [28]:…”

Section: Texture Information Comparison Functionmentioning

confidence: 99%

An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle

et al. 2012

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In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. Its energy transition function is formulated based on global image information difference and local image information difference using different energy transfer strategies. First, an energy decrease strategy is used for modeling the spatial relation information of pixels. For modeling global image information difference, a seed information comparison function is developed using an energy preserve strategy. Then, a texture information comparison function is proposed for considering local image difference in different regions, which is helpful for handling blurry boundaries. Moreover, two neighborhood systems (von Neumann and Moore neighborhood systems) are integrated as the evolution environment, and a similarity-based criterion is used for suppressing noise and reducing computation complexity. The proposed method was applied to 205 clinical BUS images for studying its characteristic and functionality, and several overlapping area error metrics and statistical evaluation methods are utilized for evaluating its performance. The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.

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“…These include Bayes classifiers assuming multivariate Gaussian distributions for the features [3], [4], [5], [6]; Fisher transformation [7], [8]; nonparametric nearest neighbor classification [9], [10], [11], [12]; classification trees [8]; learning vector quantization [13], [14]; feed-forward neural networks [15]; and recently support vector machine [16,17] and multiple histogram combined with self organized map [18] . In some earlier cases, the statistical modelling after the feature extraction is just thresholding [19], [20], [21], [22]; or simple extremum picking [23], [24], [25].…”

Section: Literaturementioning

confidence: 99%

A study of Gaussian mixture models of color and texture features for image classification and segmentation

Permuter

Francos

Jermyn

2006

Pattern Recognition

316

135

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) 'A study of Gaussian mixture models of color and texture features for image classi cation and segmentation.', Pattern recognition., 39 (4). pp. 695-706. Further information on publisher's website:http://dx.doi.org/10.1016/j.patcog.2005.10.028Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThe aims of this paper are two-fold: to define Gaussian mixture models of colored texture on several feature spaces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct Gaussian mixtures models over a variety of different color and texture feature spaces, with a view to the retrieval of textured color images from databases. We compare supervised classification results for different choices of color and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the 'color' and 'structure' information in order to improve the classification performances. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.

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A Comparative Study of Texture Measures for Terrain Classification

Cited by 1,301 publications

References 2 publications

A clustering fuzzy approach for image segmentation

A clustering fuzzy approach for image segmentation

An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle

A study of Gaussian mixture models of color and texture features for image classification and segmentation

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