Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands

Çelik, Turgay; Tjahjadi, Tardi

doi:10.1016/j.compeleceng.2011.06.008

Cited by 17 publications

(5 citation statements)

References 18 publications

(55 reference statements)

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“…Through this study, the authors clearly present their experiment as the directional selectivity is enhanced by the 2D HWT by the linear combination of Hilbert transform of different Wavelet sub-bands [ 35 ]. In [ 13 , 36 ], the researchers claim that magnitude and phase spectra are combined to define texture, discriminative HF edges in an image.…”

Section: Methodsmentioning

confidence: 99%

“…Equivalent directional selectivity is possible to accomplish by adding/subtracting the different HWT pairs [ 35 ]. In [ 36 ], the researchers combine the magnitude and phase spectra for defining texture, discriminating HF edges in an image. Hence, in the activation function proposed for the Detailed Wavelet sub-bands, the Hyper-analytic wavelet phase term (the imaginary part of the complex activation) preserves the edges that occur as negative coefficients.…”

Section: Literature Surveymentioning

confidence: 99%

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Retinopathy grading with deep learning and wavelet hyper-analytic activations

Raja¹,

Balaji

2022

Vis Comput

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Recent developments reveal the prominence of Diabetic Retinopathy (DR) grading. In the past few decades, Wavelet-based DR classification has shown successful impacts and the Deep Learning models, like Convolutional Neural Networks (CNN’s), have evolved in offering the highest prediction accuracy. In this work, the features of the input image are enhanced with the integration of Multi-Resolution Analysis (MRA) and a CNN framework without costing more convolution filters. The bottleneck with conventional activation functions, used in CNN’s, is the nullification of the feature maps that are negative in value. In this work, a novel Hyper-analytic Wavelet ( HW) phase activation function is formulated with unique characteristics for the wavelet sub-bands. Instead of dismissal, the function transforms these negative coefficients that correspond to significant edge feature maps . The hyper-analytic wavelet phase forms the imaginary part of the complex activation. And the hyper-parameter of the activation function is selected such that the corresponding magnitude spectrum produces monotonic and effective activations. The performance of 3 CNN models (1 custom, shallow CNN, ResNet with Soft attention, Alex Net for DR) with spatial–Wavelet quilts is better. With the spatial–Wavelet quilts, the Alex Net for DR has an improvement with an 11% of accuracy level (from 87 to 98%). The highest accuracy level of 98% and the highest Sensitivity of 99% are attained through Modified Alex Net for DR. The proposal also illustrates the visualization of the negative edge preservation with assumed image patches. From this study, the researcher infers that models with spatial–Wavelet quilts, with the hyper-analytic activations, have better generalization ability. And the visualization of heat maps provides evidence of better learning of the feature maps from the wavelet sub-bands.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Retinopathy grading with deep learning and wavelet hyper-analytic activations

Raja¹,

Balaji

2022

Vis Comput

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“…The recall is defined for the query image I q as given below: 11) where N G is the number of top matches considered. The precision is defined as follows: where 'n' indicates the number of retrieved images, f (x) is the category of 'x', Rank(I i , I q ) returns the rank of image I i (for the query image I q ) among all images of |DB| and…”

Section: Performance Measuresmentioning

confidence: 99%

“…Texture analysis and retrieval has gained wide attention in the field of medical, industrial, document analysis, surface inspection and many more. Various algorithms have been developed for texture analysis, such as multi-scale texture classification [11], mean and variance of the wavelet coefficients [12] texture features are reported in [13][14][15]. In practice texture features can be combined with color features to improve the retrieval accuracy.…”

Section: Introductionmentioning

confidence: 99%

Dual directional multi-motif XOR patterns: A new feature descriptor for image indexing and retrieval

Vipparthi

Murala

Nagar

2015

Optik

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“…Qiao et al [11] made use of the complex wavelet frame transform, and combined the magnitude and phase of the complex coefficients into a real measure to characterize the texture. Due to the approximately shift invariance and good directional selectivity of the dual-tree complex wavelet transform (DT-CWT), Celik and Tjahjadi [12] suggested that the statistical measures of magnitude and phase of the DT-CWT coefficients are better than the DWT based method. Ji et al [13] applied the multi-fractal spectrum (MFS) analysis on wavelet coefficients to extract robust texture descriptors for texture classification.…”

Section: Introductionmentioning

confidence: 99%

Graph wavelet transform for image texture classification

Qiao

Zhao

Song

et al. 2021

IET Image Processing

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Graph is a data structure that can represent complex relationships among data. Graph signal processing, unlike traditional signal processing, explicitly considers the structure and relationship among the signal samples. Graph wavelet transform can provide a multiscale analysis for the graph signal. It is well known that texture is a region property in an image, which is characterized with the intensity and relationship among pixels. In this context of the graph signal processing framework, an image texture can be considered as the signal on the graph. Therefore, a texture classification method based on graph wavelet transform is proposed. Specifically, image textures are decomposed into multiscale components by using two-channel graph wavelet filter banks. Then the local singular value decomposition is applied to each subband. In order to improve the noise-resistant ability, the maximum, mean and median values of the local singular values of graph-wavelet transformation coefficients are extracted. Finally, the Weibull distributions are used to model those extracted values to describe the image textures. The experiments on the benchmark texture datasets are conducted to demonstrate the effectiveness of the proposed method.How to cite this article: Qiao Y-L, Zhao Y, Song C-Y, Zhang K-G, Xiang X-Z. Graph wavelet transform for image texture classification.

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Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands

Cited by 17 publications

References 18 publications

Retinopathy grading with deep learning and wavelet hyper-analytic activations

Retinopathy grading with deep learning and wavelet hyper-analytic activations

Dual directional multi-motif XOR patterns: A new feature descriptor for image indexing and retrieval

Graph wavelet transform for image texture classification

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