Rotation invariant texture descriptors based on Gaussian Markov random fields for classification

Dharmagunawardhana, Chathurika; Mahmoodi, Sasan; Bennett, Michael J.; Niranjan, Mahesan

doi:10.1016/j.patrec.2015.10.006

Cited by 22 publications

(11 citation statements)

References 43 publications

(49 reference statements)

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“…In the method presented in (Almakady et al, 2018), the 2D-GMRF based method proposed in (Dharmagunawardhana et al, 2016) is extended to 3D-GMRF for volumetric texture classification. In this method, the 3D-GMRF model is generated at each voxel, where the estimated parameters of the model are employed as texture features in addition to the mean of a processed image region.…”

Section: Three-dimensional Gaussian Markov Random Fieldmentioning

confidence: 99%

“…Markov Random Fields (MRFs) have been introduced about two decades ago as a tool for solving visual perception problems (Wang et al, 2013). One important type of MRFs is the Gaussian Markov Random Fields (GMRF) which is among texture analysis methods that has been proved to demonstrate excellent performance in characterizing textures in 2D images, and are then employed for texture classification (Chellappa and Chatterjee, 1985;Dharmagunawardhana et al, 2016) and segmentation (Dharmagunawardhana et al, 2014;Manjunath and Chellappa, 1991;Mahmoodi and Gunn, 2011;Xia et al, 2006). GMRF model parameters can capture the essential structures of texture, and it has been a popular choice for modelling textures (Tuceryan and Jain, 1993).…”

Section: Introductionmentioning

confidence: 99%

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Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification

Almakady

Mahmoodi

Conway

et al. 2020

Computer Vision and Image Understanding

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This paper proposes a set of rotation invariant features based on three dimensional Gaussian Markov Random Fields (3D-GMRF) for volumetric texture image classification. In the method proposed here, the mathematical notion of spherical harmonics is employed to produce a set of features which are used to construct the rotation invariant descriptor. Our proposed method is evaluated and compared with other method in the literature for datasets containing synthetic textures as well as medical images. The results of our experiments demonstrate excellent classification performance for our proposed method compared with state-of-the-art methods. Furthermore, our method is evaluated using a clinical dataset and show good performance in discriminating between healthy individuals and COPD patients. Our method also performs well in classifying lung nodules in the LIDC-IDRI dataset. Our results indicate that our 3D-GMRF-based method enjoys more superior performance compared with other methods in the literature.

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Section: Three-dimensional Gaussian Markov Random Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification

Almakady

Mahmoodi

Conway

et al. 2020

Computer Vision and Image Understanding

Self Cite

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show abstract

“…Remote sensing analysis, medical image interpretation, pattern recognition and content-based image retrieval are some of the image-based applications, where texture analysis plays a fundamental and important role [1,2]. Texture representations can be classified into five categories in terms of the feature types employed, namely, statistical [3], structural [4], geometrical [5], model based [6] and signal processing features [7]. The majority of existing texture analysis methods make assumption that texture images are acquired from the same viewpoint (i.e.…”

Section: Introductionmentioning

confidence: 99%

Galois Field-based Approach for Rotation and Scale Invariant Texture Classification

Shivashankar¹,

Kudari²,

Hiremath³

2018

IJIGSP

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In this paper, a novel Galois Field-based approach is proposed for rotation and scale invariant texture classification. The commutative and associative properties of Galois Field addition operator are useful for accomplishing the rotation and scale invariance of texture representation. Firstly, the Galois field operator is constructed, which is applied to the input textural image. The normalized cumulative histogram is constructed for Galois Field operated image. The bin values of the histogram are considered as rotation and scale invariant texture features. The classification is performed using the K-Nearest Neighbour classifier. The experimental results of the proposed method are compared with that of Rotation Invariant Local Binary Pattern (RILBP) and Log-Polar transform methods. These results obtained using the proposed method are encouraging and show the possibility of classifying texture successfully irrespective of its rotation and scale.

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“…Model-based methods function according to probability distributions in random fields in characterizing the texture images. The commonly used model-based methods are Autoregressive Model (AR) [6,7], Markov Chains and Markov Random Fields (MRFs) [8,9], Wold decomposition model [10] and spatial autocorrelation function model [11]. Many various filtering techniques are used for decomposition of texture images in the methods based on signal processing technique.…”

Section: Introductionmentioning

confidence: 99%

Texture Classification System Based on 2D-DOST Feature Extraction Method and LS-SVM Classifier

Yıldırım¹,

Baloğlu²

2017

SDÜ Fen Bil Enst Der

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Abstract:In this paper, a new 2D-DOST (Two-Dimensional Discrete Orthonormal Stockwell Transform) and LS-SVM (Least Squares Support Vector Machines) based classifier system is proposed for classification of texture images. The proposed system contains two main stages. These stages are feature extraction and classification. In the feature extraction stage, the distinguishing feature vectors which represent descriptive features of texture images are obtained by using a 2D-DOST based feature extraction method. In the classification stage, the texture images are classified by the LS-SVM since this classifier has high success rate and accuracy. The training of LS-SVM is performed on the distinguishing feature vector of each texture component. Texture samples are recognized by the test data applied to the input of trained LS-SVM classifier. Performance evaluations of the proposed method are carried on different datasets obtained from sub-images. These datasets include both the normal texture images and noise added images. Sub-images into datasets are derived from Brodatz and Kylberg texture images database. Gaussian and Salt & Pepper noise with different levels are used for creating noisy datasets. According to the study results, the proposed 2D-DOST and LS-SVM based classifier has a capability of classifying texture images with high success rate and noise robustness. 2D-DOST

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Rotation invariant texture descriptors based on Gaussian Markov random fields for classification

Cited by 22 publications

References 43 publications

Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification

Rotation invariant features based on three dimensional Gaussian Markov random fields for volumetric texture classification

Galois Field-based Approach for Rotation and Scale Invariant Texture Classification

Texture Classification System Based on 2D-DOST Feature Extraction Method and LS-SVM Classifier

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