Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes

Porter, Robert P.; Canagarajah, N.

doi:10.1049/ip-vis:19971182

Cited by 164 publications

(113 citation statements)

References 8 publications

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“…We demonstrate the performance of our operators with imagery used in a recent study on rotation invariant texture classification by Porter and Canagarajah [11]. They presented three feature extraction schemes for rotation invariant texture classification, employing the wavelet transform, a circularly symmetric Gabor filter and a Gaussian Markov Random Field with a circularly symmetric neighbor set.…”

Section: Methodsmentioning

confidence: 98%

“…The performance of the proposed approach is demonstrated with an image data used in a recent study on rotation invariant texture classification [11]. Excellent experimental results demonstrate that our texture representation learned at a specific rotation angle generalizes to other rotation angles.…”

Section: Introductionmentioning

confidence: 89%

“…effect of blurring on rotation at other angles [11]. In the original experimental setup the texture classifier was trained with several 16x16 subimages extracted from the training image.…”

Section: Image Data and Experimental Setupmentioning

confidence: 99%

“…However, real world textures can occur at arbitrary spatial resolutions and rotations and they may be subjected to varying illumination conditions. This has inspired a collection of studies, which generally incorporate invariance with respect to one or at most two of the properties spatial scale, orientation and gray scale, among others [1,2,3,4,5,6,7,8,10,11,13,14].…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification

Ojala

Pietikäinen

Mäenpää

2001

Lecture Notes in Computer Science

234

113

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Abstract. This paper presents generalizations to the gray scale and rotation invariant texture classification method based on local binary patterns that we have recently introduced. We derive a generalized presentation that allows for realizing a gray scale and rotation invariant LBP operator for any quantization of the angular space and for any spatial resolution, and present a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray scale variations, since the operator is by definition invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity, as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in a true problem of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis. nonparametric texture analysis distribution histogram Brodatz contrast

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Section: Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 89%

“…effect of blurring on rotation at other angles [11]. In the original experimental setup the texture classifier was trained with several 16x16 subimages extracted from the training image.…”

Section: Image Data and Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification

Ojala

Pietikäinen

Mäenpää

2001

Lecture Notes in Computer Science

234

113

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“…Haralick et al (1973) propose computing omnidirectional features from gray level co-occurrence matrix and Mayorga and Ludeman (1994) employ isotropic texture edge statistics based on circular levels or neighbourhoods. The features extracted from filter responses achieved via isotropic filter kernels have also been proposed and higher texture classification rates have been reported (Porter and Canagarajah, 1997;Zhang et al, 2002). Furthermore, model based approaches such as Circular Simultaneous Auto Regression (CSAR) and its extensions Multiresolution Rotation Invariant Simultaneous Auto Regression (MR-RISAR) model (Kashyap and Khotanzad, 1986;Mao and Jain, 1992) are introduced which employ isotropic model parameters as texture features.…”

Section: Rotation Invariant Texture Descriptorsmentioning

confidence: 99%

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

Dharmagunawardhana

Mahmoodi

Bennett

et al. 2016

Pattern Recognition Letters

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Rotation and scale invariant wavelet feature for content‐based texture image retrieval

Lee¹,

Pun²

2002

J. Am. Soc. Inf. Sci.

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This article introduces an effective rotation and scale invariant log-polar wavelet texture feature for image retrieval. The proposed feature is an attempt to enhance the existing content-based image retrieval systems that largely present difficulty in coping with images with changes in orientations and scales. The underlying feature extraction process involves a log-polar transform followed by an adaptive row shift invariant wavelet packet transform. The log-polar transform converts a given image into a rotation and scale invariant but rowshifted image, which is then further processed through an adaptive row-shift invariant wavelet packet transform operation to generate adaptively selected subbands of rotation and scale invariant wavelet coefficients, based on an information cost function. An energy signature is computed for each subband of these wavelet coefficients. To reduce feature dimensionality, only the most dominant log-polar wavelet energy signatures are selected for the feature vector for image retrieval. The overall feature extraction process is quite efficient and involves only O(n ⅐ log n) complexity. Experimental results show that this rotation and scale invariant wavelet feature is quite effective for image retrieval and outperforms the traditional wavelet packet signatures.

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Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes

Cited by 164 publications

References 8 publications

A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification

A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification

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

Rotation and scale invariant wavelet feature for content‐based texture image retrieval

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