Rotation-invariant texture classification using feature distributions

Pietikäinen, Matti; Ojala, Timo; Xu, Zelin

doi:10.1016/s0031-3203(99)00032-1

Cited by 392 publications

(170 citation statements)

References 17 publications

(26 reference statements)

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“…The potential areas include industrial and biomedical surface inspection, ground classification and segmentation of satellite or aerial imagery, document analysis, scene analysis, texture synthesis for computer graphics and animation, biometric person authentication, content-based image retrieval and modelbased image coding [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

SASI: a generic texture descriptor for image retrieval

Carkacioglu

Yarman-Vural

2003

Pattern Recognition

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In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Gabor Filter descriptors in capturing small granularities and discontinuities such as sharp corners and abrupt changes. Due to the flexibility in designing the clique windows, SASI reaches higher average retrieval rates compared to Gabor Filter descriptors. However, the price of this performance is increased computational complexity.

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Section: Introductionmentioning

confidence: 99%

SASI: a generic texture descriptor for image retrieval

Carkacioglu

Yarman-Vural

2003

Pattern Recognition

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“…Polar plots and polarogram is another approach based on polar transformation. Furthermore, feature distributions of locally invariant features such as linear symmetric auto correlation measures, related covariance measures, rotation invariant local binary patterns and gray level difference have been successfully employed as rotation invariant features (Pietikäinen et al, 2000). The local features are made invariant based on neighbourhood operations such as circular shifting.…”

Section: Rotation Invariant Texture Descriptorsmentioning

confidence: 99%

“…TGMRF features describe spatial pixel dependencies which is a primary characteristic associated with texture. However, these features ignore some important structural and statistical information about the texture and have performed poorly (Ojala et al, 2001;Hadjidemetriou et al, 2003;Pietikäinen et al, 2000;Petrou and Sevilla, 2006;Liu and Wang, 2003). Therefore in recent work, we proposed Local Parameter Histogram (LPH) descriptor which is an improved texture descriptor demonstrating significant improvement in characterizing texture compared to the TGMRF descriptors (Dharmagunawardhana et al, 2014b) .…”

Section: Introductionmentioning

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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“…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%

“…Recently, we introduced a theoretically and computationally simple approach for gray scale and rotation invariant texture analysis based on Local Binary Patterns, which is robust in terms of gray scale variations and discriminated rotated textures efficiently [8,10]. A novel contribution of this paper is the generalized presentation of the operator that allows for realizing it for any quantization of the angular space and for any spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

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

Ojala

Pietikäinen

Mäenpää

2001

Lecture Notes in Computer Science

224

109

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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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Rotation-invariant texture classification using feature distributions

Cited by 392 publications

References 17 publications

SASI: a generic texture descriptor for image retrieval

SASI: a generic texture descriptor for image retrieval

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

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

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