Multiwindowed approach to the optimum estimation of the local fractal dimension for natural image segmentation

Novianto, Sonny; Guimarães, Letícia Vieira; Suzuki, Yusuke; Maeda, Junji; Anh, Vo

doi:10.1109/icip.1999.817105

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…[21,24,28,30,33]). Their performance basically depends on the type of processing they apply, the size of the neighborhood of pixels over which they are evaluated (window size) and the texture content.…”

Section: Introductionmentioning

confidence: 97%

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Supervised texture classification by integration of multiple texture methods and evaluation windows

García

Puig

2007

Image and Vision Computing

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Section: Introductionmentioning

confidence: 97%

“…[6,22,28]), few have dealt with the issue of determining optimal window sizes, and they do it for specific texture methods (e.g., [3,8,21]). Hence, window sizes are commonly defined on an experimental basis, with each method being evaluated over windows of a single size.…”

Section: Introductionmentioning

confidence: 99%

Supervised texture classification by integration of multiple texture methods and evaluation windows

García

Puig

2007

Image and Vision Computing

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“…The majority of those works find out optimal sizes for specific texture methods (e.g., [2] [10]). In the scope of pixel-based texture classification, [12] presents a tech-nique for determining the window size that leads to the maximum separability among texture models, given an arbitrary texture method and a set of texture models of interest.…”

Section: Introductionmentioning

confidence: 99%

Pixel-Based Texture Classification by Integration of Multiple Texture Feature Evaluation Windows

Puig

García

2003

Pattern Recognition and Image Analysis

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A wide variety of texture feature extraction methods have been proposed for texture based image classification and segmentation. These methods are typically evaluated over windows of the same size, the latter being usually chosen for each particular method on an experimental basis. This paper shows that pixel-based texture classification can be significantly improved by evaluating a given texture method over multiple windows of different size and then by integrating the results through a classical Bayesian scheme. The proposed technique has been applied to well-known families of texture methods that are frequently utilized for feature extraction from textured images. Experiments show that the integration of multisized windows yields lower classification errors than when optimal single-sized windows are considered.

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“…If the change of the difference is high, there is a tendency to exist more than one kind of textures or image elements within the window, thus the small size of the window should be adopted. Otherwise, the current size of the window is appropriate for the estimation of that position [Novianto et al, 1999]. The criteria for the selection of window size are based on the resolution, classification specificity and the nature of the classes.…”

Section: Fractal Feature Vectormentioning

confidence: 99%

Contextual PolSAR image classification using fractal dimension and support vector machines

Aghababaee

Amini

Tzeng

2013

European Journal of Remote Sensing

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In this paper, a new classification scheme of polarimetric synthetic aperture radar (PolSAR) images using fractal dimension as contextual information is proposed. Support vector machines (SVM) due to their ability to handle the nonlinear classifier problem are applied to a new fractal feature vector, which is constructed from Pauli decomposed vector and fractal dimensions. Fractal dimension is computed based on the concepts of fractional Brownian motion (fBm) and wavelet multi-resolution analysis using a self-adaptive window approach and fuzzy logic. The experimental results on AIRSAR images prove effectiveness of the proposed vector in comparison to the Pauli decomposed vector.

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Multiwindowed approach to the optimum estimation of the local fractal dimension for natural image segmentation

Cited by 7 publications

References 7 publications

Supervised texture classification by integration of multiple texture methods and evaluation windows

Supervised texture classification by integration of multiple texture methods and evaluation windows

Pixel-Based Texture Classification by Integration of Multiple Texture Feature Evaluation Windows

Contextual PolSAR image classification using fractal dimension and support vector machines

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