Contourlet-Based Texture Classification with Product Bernoulli Distributions

Dong, Yongsheng; Ma, Jinwen

doi:10.1007/978-3-642-21090-7_2

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Many recent studies showed contourlets as an efficiently directional multiscale image representation scheme, mainly in capturing the intrinsic geometrical structure as a key in visual information [1][2][3][4][5][6][7][8][9]. Their experiments in many image processing applications showed that the contourlets can significantly outperform some other transforms (e.g., wavelets) both in visual quality and in terms of quality assessment index.…”

Section: Introductionmentioning

confidence: 99%

Tunable-Q contourlet transform for image representation

Wang

Yang

et al. 2013

J. of Syst. Eng. Electron.

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A novel tunable-quality-factor (tunable-Q) contourlet transform for geometric image representation is proposed. The Laplacian pyramid in original contourlet decomposes a signal into channels that have the same bandwidth on a logarithmic scale, and is not suitable for images with different behavior in frequency domain. We employ a new tunable-Q decomposition defined in the frequency domain by which one can flexibly tune the bandwidth of decomposition channels. With an acceptable redundancy, this tunable-Q contourlet is also anti-aliasing and its basis is sharply localized in the desired area of frequency and spatial domain. Our experiments in nonlinear approximation and denoising show that the contourlet using a better-suitable quality factor can achieve a more promising performance and often outperform wavelets and the previous contourlets both in visual quality and in terms of peak signal-to-noise ratio.

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Section: Introductionmentioning

confidence: 99%

Tunable-Q contourlet transform for image representation

Wang

Yang

et al. 2013

J. of Syst. Eng. Electron.

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“…Up to now, a variety of texture representation/classification methods have been proposed. They include statistical methods [26], [27], structure methods [28], [29], model methods [30], and transform domain based methods [31], [32]. As a typical representation of statistical methods, local binary pattern (LBP) was proposed to represent image texture [33].…”

Section: Introductionmentioning

confidence: 99%

Locally Directional and Extremal Pattern for Texture Classification

et al. 2019

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An image texture was defined in terms of pixel intensities and directionality. However, most of the current texture representation methods did not consider the two key factors simultaneously. To effectively capture the directional and pixel intensity information of texture, in this paper, we propose a novel and robust local descriptor, named locally directional and extremal pattern (LDEP), for texture classification. It extracts directional local difference count pattern (DLDCP) being made up of DLDCP in the odd positions and DLDCP in the even positions to express directional information in the local area in the first place. Furthermore, to acquire the extremum information remained by DLDCP, by concatenating extremum location pattern (ELP), extremum difference pattern (EDP), and extremum compression pattern (ECP) from the sampling points, we extract a neighbors extremum related local pattern (NERLP). The experimental results obtained from four representative texture databases (Prague, Stex, UIUC, Kth-tips2-a, Brodatz, and CUReT) demonstrate that our proposed LDEP descriptor can achieve comparable accurate classification rates in different conditions (rotation, illumination, scale variation, viewpoint variation, and noise) with ten typical texture classification methods. INDEX TERMS Directional pattern, extremal pattern, local pattern, texture representation, texture classification.

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“…Methods used for texture classification can be divided into transform-based [6][7][8], statistic [9][10][11], model-based [12][13][14] and structure-based methods [15][16][17][18][19]. Among them, studies of the structure-based method have been done for many years, and numbers of approaches have been proposed.…”

Section: Introductionmentioning

confidence: 99%