Local multiple patterns based multiresolution gray-scale and rotation invariant texture classification

Zhu, Chenchen; Wang, Runsheng

doi:10.1016/j.ins.2011.10.014

Cited by 62 publications

(21 citation statements)

References 33 publications

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“…Among them, several features were used to recognize coin images [3,7,8,10,14]. Representatively, rotation-invariant local binary patterns were proposed [15][16][17][18]. These features could be robust against rotation by grouping the uniform patterns [15][16][17] or applying Fourier transform [18], and were applied to image-based by [10,14].…”

Section: Comparing With Previous Approachesmentioning

confidence: 99%

“…Representatively, rotation-invariant local binary patterns were proposed [15][16][17][18]. These features could be robust against rotation by grouping the uniform patterns [15][16][17] or applying Fourier transform [18], and were applied to image-based by [10,14]. Also, CSGabor was designed to be robust to rotation [3].…”

Section: Comparing With Previous Approachesmentioning

confidence: 99%

“…Since LBP features were extracted by comparing a center pixel and its neighbors in an image patch [12], the extraction time was much faster than that of Gabor filter approaches. Also, there were several rotation-invariant LBP approaches, which grouped binary patterns [15][16][17] or used Fourier transformation [18]. Most methods using LBP for image-based coin recognition [3,10,14] divided a coin image into several rings and extracted LBPs.…”

Section: Introductionmentioning

confidence: 99%

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Image-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes

Kim

Lee

2015

Journal of Visual Communication and Image Representation

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Section: Comparing With Previous Approachesmentioning

confidence: 99%

Section: Comparing With Previous Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes

Kim

Lee

2015

Journal of Visual Communication and Image Representation

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“…An extension of LBP is LMP from binary patterns to multiple patterns made to be more robust for image analysis, including the analysis of flat image areas (Zhu & Wang, 2012). Number of middle patterns between 0 and 1 are added which led LMP more informative than LBP.…”

Section: Local Multiple Pattern (Lmp)mentioning

confidence: 99%

Land use land cover classification using local multiple pattern from very high resolution satellite imagery

Kumar

Balaji

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The recent development in satellite sensors provide images with very high spatial resolution that aids detailed mapping of Land Use Land Cover (LULC). But the heterogeneity in the landscapes often results in spectral variation within the same and spectral confusion among different LU/LC classes at finer spatial resolution. This leads to poor classification performances based on traditional spectral-based classification. Many studies have been addressed to improve this classification by incorporating texture information with multispectral images. Although different methods are available to extract textures from the satellite images, only a limited number of studies compared their performance in classification. The major problem with the existing texture measures is either scale/orientation/illumination variant (Haralick textures) or computationally difficult (Gabor textures) or less informative (Local binary pattern). This paper explores the use of texture information captured by Local Multiple Patterns (LMP) for LULC classification in a spectral-spatial classifier framework. LMP preserve more structural information and involves less computational efforts. Thus LMP is expected to be more promising for capturing spatial information from very high spatial resolution images. The proposed method is implemented with spectral bands and LMP derived from WorldView-2 multispectral imagery acquired for Madurai, India study area. The Multi-Layer-Perceptron neural network is used as a classifier. The proposed classification method is compared with LBP and conventional Maximum Likelihood Classification (MLC) separately. The classification results with 89.5% clarify the improvement offered by the LMP for LULC classification in comparison with the conventional approaches.

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“…It started by the revolutionary approach derived by Ojala et alto derive texture features by quantizing the local pixel values of a neighborhood in to two values and named it as local binary patterns (LBPs) [11,12]. Later several authors [13][14][15][16][17][18][19] carried out abundant work and derived efficient methods to further extend the benefits of LBP in various applications. The Binary features [12,13,15,20,21,22] gained reputation and recognition due to their efficient design, computational simplicity and good performance.…”

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confidence: 99%

Texture Classification based on First Order Local Ternary Direction Patterns

Rao¹,

Kumar²,

Prasad³

2017

IJIGSP

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Abstract-The local binary pattern (LBP) and local ternary pattern (LTP) are basically gray scale invariant, and they encode the binary/ ternary relationship between the neighboring pixels and central pixel based on their grey level differences and derives a unique code. These traditional local patterns ignore the directional information. The proposed method encodes the relationship between the central pixel and two of its neighboring pixel located in different angles (α, β) with different directions. To estimate the directional patterns, the present paper derived variation in local direction patterns in between the two derivates of first order and derived a unique First order -Local Direction variation pattern (FO-LDVP)

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Local multiple patterns based multiresolution gray-scale and rotation invariant texture classification

Cited by 62 publications

References 33 publications

Image-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes

Image-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes

Land use land cover classification using local multiple pattern from very high resolution satellite imagery

Texture Classification based on First Order Local Ternary Direction Patterns

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