Multiscale Sampling Based Texture Image Classification

Dong, Yongsheng; Feng, Jinwang; Liang, Lin-Mei; Zheng, Lintao; Wu, Qingtao

doi:10.1109/lsp.2017.2670026

Cited by 40 publications

(9 citation statements)

References 28 publications

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“…The sub-images obtained are then used to extract scale dependent texture features such as energy, entropy, variance, refined histogram, etc (see [152] for examples of wavelet-based signatures). In 2017, based on the wavelet transform, Dong et al [153] proposed a multiscale rotationinvariant representation of textures by using multiscale sampling. In 2018, Yang et al [154] proposed the association of the dual-tree complex wavelet transform and LBP for rotation, illumination, and scale invariant texture classification.…”

Section: Wavelet-based Approaches 1) Conceptmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract-from raw images-meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropybased approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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Section: Wavelet-based Approaches 1) Conceptmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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“…Considering that scale variation and viewpoint variation can also affect the classification performance, we employed wavelet transforms to perform multi-scale feature extraction on the projected image (symbol information and magnitude information) [39]. Arashloo et al used a learning filter to linearly place a local area of the image on the subspace for operation [40].…”

Section: Related Workmentioning

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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“…Here, a short review of some texture recognition methods are given. Multiscale rotation-invariant sampling based texture image classification is proposed in [13]. A multiscale wavelet transform is used to decompose the magnitude pattern (MP) mapping of a texture and the sampled directional mean vectors (SDMVs) of each wavelet subband is computed.…”

Section: Related Workmentioning

confidence: 99%

Bag of recurrence patterns representation for time-series classification

Hatami

Gavet

Debayle

2018

Pattern Anal Applic

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Time-Series Classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals. Bag of Features (BoF) model has achieved a great success in TSC task by summarizing signals according to the frequencies of "feature words" of a data-learned dictionary. This paper proposes embedding the Recurrence Plots (RP), a visualization technique for analysis of dynamic systems, in the BoF model for TSC. While the traditional BoF approach extracts features from 1D signal segments, this paper uses the RP to transform time-series into 2D texture images and then applies the BoF on them. Image representation of time-series enables us to explore different visual descriptors that are not available for 1D signals and to treats TSC task as a texture recognition problem. Experimental results on the UCI time-series classification archive demonstrates a significant accuracy boost by the proposed Bag of Recurrence patterns (BoR), compared not only to the existing BoF models, but also to the state-of-the art algorithms.

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Multiscale Sampling Based Texture Image Classification

Cited by 40 publications

References 28 publications

Texture Feature Extraction Methods: A Survey

Texture Feature Extraction Methods: A Survey

Locally Directional and Extremal Pattern for Texture Classification

Bag of recurrence patterns representation for time-series classification

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