Multi-Scale Shape Index-Based Local Binary Patterns for Texture Classification

Alpaslan, Nuh; Hanbay, Kazım

doi:10.1109/lsp.2020.2987474

Cited by 24 publications

(18 citation statements)

References 33 publications

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“…e aim of this section is to validate our proposed CWTA-CapsNet on three datasets: CUReT [40], DTD [41], and KTH-TIPS2-b [42]. For the CUReT dataset, we use the same subset as in [43] Besides CWTACapsNet, five state-of-the-art methods, T-CNN [13], FV-CNN [8], SI-LCvMSP [1], Wavelet CNNs [11], and CapsNet [44], are employed for performance comparison.…”

Section: Methodsmentioning

confidence: 99%

“…Models in experiments are trained under Ubuntu 16.04 with i7-8700 CPU, 64G RAM, and GeForce GTX Titan-XP GPU, and our proposed CWTACapsNet is deployed on Jetson TX2. To provide a direct comparison with published results, parameters of five state-of-the-art methods are set according to previous studies [1,8,11,13,44]. We use an exponential decay learning policy, with an initial learning rate of 0.001, 2000 decay steps, and 0.96 decay rate.…”

Section: Methodsmentioning

confidence: 99%

“…For simplicity, we assume that p can be divided by P( < p) with no reminder and p � (p/P). Let W j � [ω (1) j , ω (2) j , . .…”

Section: Quantized Capsule Networkmentioning

confidence: 99%

“…Texture classification is crucial in pattern recognition and computer vision [1][2][3][4][5]. Since many very sophisticated classifiers exist, the key challenge here is the development of effective features to extract from a given textured image [6].…”

Section: Introductionmentioning

confidence: 99%

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Compressed Wavelet Tensor Attention Capsule Network

Liu

Shan

Zhang

et al. 2021

Security and Communication Networks

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Texture classification plays an important role for various computer vision tasks. Depending upon the powerful feature extraction capability, convolutional neural network (CNN)-based texture classification methods have attracted extensive attention. However, there still exist many challenges, such as the extraction of multilevel texture features and the exploration of multidirectional relationships. To address the problem, this paper proposes the compressed wavelet tensor attention capsule network (CWTACapsNet), which integrates multiscale wavelet decomposition, tensor attention blocks, and quantization techniques into the framework of capsule neural network. Specifically, the multilevel wavelet decomposition is in charge of extracting multiscale spectral features in frequency domain; in addition, the tensor attention blocks explore the multidimensional dependencies of convolutional feature channels, and the quantization techniques make the computational storage complexities be suitable for edge computing requirements. The proposed CWTACapsNet provides an efficient way to explore spatial domain features, frequency domain features, and their dependencies which are useful for most texture classification tasks. Furthermore, CWTACapsNet benefits from quantization techniques and is suitable for edge computing applications. Experimental results on several texture datasets show that the proposed CWTACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…For simplicity, we assume that p can be divided by P( < p) with no reminder and p � (p/P). Let W j � [ω (1) j , ω (2) j , . .…”

Section: Quantized Capsule Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compressed Wavelet Tensor Attention Capsule Network

Liu

Shan

Zhang

et al. 2021

Security and Communication Networks

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“…• Model-based methods: The model-based methods take the raw representation of the 3D model as input, such as mesh, volume and point cloud. Since handcrafted features are widely used in the many fields of computer vision [28]- [31], which can well reflect the characteristics of the data. In previous work, researchers tended to design handcrafted features, like point feature histograms [32], and local surface feature descriptions [33] to recognize these raw representations.…”

Section: Related Workmentioning

confidence: 99%

Semantic and Context Information Fusion Network for View-Based 3D Model Classification and Retrieval

et al. 2020

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In recent years, with the rapid development of 3D technology, view-based methods have shown excellent performance in both 3D model classification and retrieval tasks. In view-based methods, how to aggregate multi-view features is a key issue. There are two commonly used solutions in the existing methods: 1) Use pooling strategy to merge multi-view features, but it ignores the context information contained in the continuous view sequence. 2) Leverage grouping strategy or long short term memory networks (LSTM) to select representative views of the 3D model, however, it easily neglects the semantic information of individual views. In this paper, we propose a novel Semantic and Context information Fusion Network (SCFN) to compensate for these drawbacks. First, we render views from multiple perspectives of the 3D model and extract the raw feature of the individual view by 2D convolutional neural networks (CNN). Then we design the channel attention mechanism (CAM) to exploit the view-wise semantic information. By modeling the correlation among view feature channels, we can assign higher weights to useful feature attributes, while suppressing the useless. Next, we propose a context information fusion module (CFM) to fuse multiple view features to obtain a compact 3D representation. Extensive experiments are conducted on three popular datasets, i.e., ModelNet10, ModelNet40, and ShapeNetCore55, which can demonstrate the superiority of the proposed method comparing to the state-of-the-arts on both 3D classification and retrieval tasks.

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