Marginal distribution covariance model in the multiple wavelet domain for texture representation

Li, Chaorong; Huang, Yuanyuan; Yang, Xingchun; Chen, Huafu

doi:10.1016/j.patcog.2019.04.003

Cited by 24 publications

(14 citation statements)

References 44 publications

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“…Li et al. [45] developed the marginal distribution covariance model (MDCM) in the multiple wavelet domain. The texture features were extracted by using the orthogonal wavelet transform, Gabor wavelet transform and dual‐tree complex wavelet transform for texture classification. Jet Texton Learning.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

Graph wavelet transform for image texture classification

Qiao

Zhao

Song

et al. 2021

IET Image Processing

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“…The wavelet method is generally used as a pre-processing method for signal processing [17], noise reduction [18][19], image processing [20], texture [21] which gives excellent results [22]. Wavelet consists of the decomposition that has a frequency component and reconstruction to the time domain.…”

Section: Wavelet Extractionmentioning

confidence: 99%

Post-Stroke identification of EEG signals using recurrent neural networks and long short-term memory

Sansiagi

Djamal

Djajasasmita

et al. 2021

Int. J. Adv. Intell. Informatics

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Stroke often causes disability, so patients need rehabilitation for recovery. Therefore, it is necessary to measure its effectiveness. An Electroencephalogram (EEG) can capture the improvement of activity in the brain in stroke rehabilitation. Therefore, the focus is on the identification of several post-rehabilitation conditions. This paper proposed identifying post-stroke EEG signals using Recurrent Neural Networks (RNN) to process sequential data. Memory control in the use of RNN adopted Long Short-Term Memory. Identification was provided out on two classes based on patient condition, particularly "No Stroke" and "Stroke". EEG signals are filtered using Wavelet to get the waves that characterize a stroke. The four waves and the average amplitude are features of the identification model. The experiment also varied the weight correction, i.e., Adaptive Moment Optimization (Adam) and Stochastic Gradient Descent (SGD). This research showed the highest accuracy using Wavelet without amplitude features of 94.80% for new data with Adam optimization model. Meanwhile, the feature configuration tested effect shows that the use of the amplitude feature slightly reduces the accuracy to 91.38%. The results also show that the effect of the optimization model, namely Adam has a higher accuracy of 94.8% compared to SGD, only 74.14%. The number of hidden layers showed that three hidden layers could slightly increase the accuracy from 93.10% to 94.8%. Therefore, wavelets as extraction are more significant than other configurations, which slightly differ in performance. Adam's model achieved convergence in earlier times, but the speed of each iteration is slower than the SGD model. Experiments also showed that the optimization model, number of epochs, configuration, and duration of the EEG signal provide the best accuracy settings.

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“…For example, if linearly weighted summation is used, the final label of image i would be given to the class with arg max C . Let A i and B i are the components of vectors A and B, respectively, the distance metrics d can be calculated using cosine similarity metric learning [42] as given in (7). The ELCS process is further elaborated in Algorithm 3 ( Fig.…”

Section: Face Classification Using Ensembles Of Cosine Similarity Metmentioning

confidence: 99%

Locally lateral manifolds of normalised Gabor features for face recognition

Zaman

2020

IET Computer Vision

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Due to inherent characteristics of multiscale and orientation, normalised Gabor features have been successfully used in face recognition. Various previous works have showcased the strength and feasibility of this approach, especially on its robustness against local variations. However, the projected features are numerous and substantial in dimension, which is largely due to the convolution of multiscale and orientation of wavelets. Such features, when used in practical face recognition, would require relatively lengthy classification process, particularly when it involves computationally extensive local classifier or experts, such as ensembles of local cosine similarity (ELCS) classifier. The authors address this issue by simultaneously reducing the size of Gabor features laterally and locally using a manifold learning method called locally linear embedding (LLE). This method is thus denoted as locally lateral normalised local Gabor feature vector with LLE (LGFV/LN/LLE). Results on several publicly available face datasets reveal the superiority of the authors’ approach in terms of improvements in feature compression of LGFV features by up to a reduction of 95% of total dimensionality while increasing the average classification accuracy by 26%. Altogether, the authors show that their LGFV/LN/LLE augmented by ELCS classifiers delivers equivalent result when compared against the state‐of‐the‐art.

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Marginal distribution covariance model in the multiple wavelet domain for texture representation

Cited by 24 publications

References 44 publications

Graph wavelet transform for image texture classification

Graph wavelet transform for image texture classification

Post-Stroke identification of EEG signals using recurrent neural networks and long short-term memory

Locally lateral manifolds of normalised Gabor features for face recognition

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