Color texture classification by a discrete statistical model and feature selection

Bouguila, Nizar; Hammoud, Riad I.

doi:10.1109/icip.2009.5413376

Cited by 6 publications

(4 citation statements)

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“…Generally the Gaussian mixture has been adopted in the literature. However, this choice has been found inappropriate in the case of count data [11], [12], [13], [14], [15] which are naturally generated by several applications from different disciplines such as spam filtering where a given email is generally represented as a vector of words frequencies [16], [17], [18], [19] and visual scenes categorization where a given image or texture can be represented as a count vector of visual words [13], [20], [21]. Another problem is the fact that real life applications are generally characterized by masses of noisy, high-dimensional and sparse data (see, for instance, [22]).…”

Section: Introductionmentioning

confidence: 99%

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Bouguila

2011

2011 IEEE 23rd International Conference on Tools With Artificial Intelligence

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This paper presents an unsupervised statistical model for simultaneous clustering, feature selection and outlier rejection in the case of count data. The proposed model is based on a finite discrete mixture to which a uniform component is added to ensure robustness to outliers and noise. The consideration of a finite mixture model is justified by its flexibility, its solid grounding in the theory of statistics and its competitive results. We derive a complete maximum a posteriori learning approach that does not require a priori knowledge about the number of outliers and the number of clusters. A rigorous expectation maximization (EM) algorithm, based on the formulation of a maximum a posteriori (MAP) estimation, is also provided. We report experimental results of applying our model to the challenging problems of visual scenes categorization and texture discrimination.

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Section: Introductionmentioning

confidence: 99%

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Bouguila

2011

2011 IEEE 23rd International Conference on Tools With Artificial Intelligence

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“…Texture analysis is an important generic research area of machine vision, which can be applied in a variety of image processing applications such as industrial inspection [1], medical imaging [2], remote sensing [3], and content-based image classification and retrieval [4], [5], [6], [7], [8]. Regarding its importance, many research works have been done on texture analysis and classification.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach for texture images classification and retrieval

Bouguila

Elguebaly

2011

2011 International Conference on Multimedia Computing and Systems

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Texture analysis plays an important role in many image processing and computer vision tasks, ranging from natural to medical imaging and content-based image retrieval. In this paper, we present an efficient Bayesian algorithm for texture image classification and retrieval, based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) and general Beta mixture models. Our work is motivated by the fact that textured images are generally described by non-Gaussian characteristics which cannot be realistically modeled using rigid distributions. Beta mixtures are able to fit any unknown distributional shape and then can be considered as a useful and flexible solution for the problem of modeling non-Gaussian features present in texture images. In theory, it is well-known that full Bayesian approaches, to handle the mixture estimation and selection problems, are fully optimal. We applied then a fully Bayesian, RJMCMC, technique which simultaneously allows cluster assignments, parameters estimation, and the selection of the optimal number of clusters. Experimental results involving a challenging texture images data set are presented and discussed to show the merits of the proposed work.

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“…In the image, texture features capture information about repeating patterns. Texture analysis can be classified into three models: structural, statistical, and signal theoretic methods [3]. Therefore, the analysis of texture parameters is a useful approach for increasing the information accessible from images.…”

Section: Introductionmentioning

confidence: 99%

Texture Enhancement Based on the Savitzky-Golay Fractional Differential Operator

Jalab

Ibrahim

2013

Mathematical Problems in Engineering

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Texture enhancement for digital images is the most important technique in image processing. The purpose of this paper is to design a texture enhancement technique using fractional order Savitzky-Golay differentiator, which leads to generalizing the Savitzky-Golay filter in the sense of the Srivastava-Owa fractional operators. By employing this generalized fractional filter, texture enhancement is introduced. Consequently, it calculates the generalized fractional order derivative of the given image using the sliding weight window over the image. Experimental results show that the operator can extract more subtle information and make the edges more prominent. In general, the capability of the generalized fractional differential will be high because it is sensitive to the subtle fluctuations of values of pixels.

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Color texture classification by a discrete statistical model and feature selection

Cited by 6 publications

References 13 publications

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

A Bayesian approach for texture images classification and retrieval

Texture Enhancement Based on the Savitzky-Golay Fractional Differential Operator

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