Sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) for feature extraction

Wan, Minghua; Yang, Guowei; Sun, Chengjie; Liu, Maoxi

doi:10.1007/s00500-018-3207-9

Cited by 15 publications

(8 citation statements)

References 38 publications

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“…In other words, the applied normalization method achieve the effect of improving the classification accuracy. We further illustrate that the LP-DMI-HOG descriptor is more efficient and more discriminative than the descriptor extracted by VGG-16 [52] as depicted in Table 4.…”

Section: Parameter Selectionmentioning

confidence: 80%

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Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences

Huang

et al. 2021

Preprint

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Human action recognition is an active research area in computer vision. Although great process has been made, previous methods mostly recognize actions based on depth data at only one scale, and thus they often neglect multi-scale features that provide additional information action recognition in practical application scenarios. In this paper, we present a novel framework focusing on multiscale motion information to recognize human actions from depth video sequences. We propose a multi-scale feature map called Laplacian pyramid depth motion images(LP-DMI). We employ depth motion images (DMI) as the templates to generate the multi-scale static representation of actions. Then, we caculate LP-DMI to enhance multi-scale dynamic information of motions and reduces redundant static information in human bodies. We further extract the multi-granularity descriptor called LP-DMI-HOG to provide more discriminative features. Finally, we utilize extreme learning machine (ELM) for action classification. The proposed method yeilds the recognition accuracy of 93.41%, 85.12%, 91.94% on public MSRAc-tion3D dataset, UTD-MHAD and DHA dataset. Through extensive experiments, we prove that our method outperforms state-of-the-art benchmarks.

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Section: Parameter Selectionmentioning

confidence: 80%

“…There are several reasonable options for determining which feature to extract [50][51][52]. In this paper, we utilize HOG descriptors to extract the local features of LP-DMI denoted as LP-DMI-HOG.…”

Section: Feature Extraction and Action Classificationmentioning

confidence: 99%

Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences

Huang

et al. 2021

Preprint

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“…To address this issue, He et al [10] put forward the LPP method, which ensures that the structural relationship of data samples in low-dimensional space is consistent with that in high-dimensional space. Inspired by LPP, many related feature extraction algorithms have been widely developed, such as local graph embedding based on maximum margin criterion [11], fuzzy 2D discriminant LPP (F2DDLPP) [12] and sparse 2DDLPP (S2DDLPP) [13]. The method proposed in [11] effectively solves the influence of some physical changes on image recognition, such as illumination, posture and expression.…”

Section: Introductionmentioning

confidence: 99%

Application of Locally Invariant Robust PCA for Underwater Image Recognition

2021

IEEE Access

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Recently, many PCA with robust low-dimensional representation models have been applied in imaging. However, most models ignore the manifold geometry of the data and fail to minimize the reconstruction error. Here, a novel robust PCA structure, locally invariant robust principal component analysis (LIRPCA), is proposed for underwater image recognition. The contributions of LIRPCA are as follows: (1) LIRPCA selects the 2 l-norm as distance metric criterion to describe the global geometry and the intrinsic geometry, which ensures the robustness of the overall model structure. (2) LIRPCA constructs a close relationship between the reconstruction error of the projected data and the input data in the cost function to minimize the reconstruction error. (3) To solve the challenging optimization function of LIRPCA, we design an iterative algorithm with fast convergence to obtain the desired solution. The proposed model is applied to feature extraction and recognition tasks from several underwater image datasets and performs better than other models. INDEX TERMS Manifold geometry, Principal component analysis, 2 l-norm, Underwater image recognition.

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“…ese connections exist not only in the spatial position but also in the frequency domain. As described above, the main focus is on alpha band (8)(9)(10)(11)(12)(13) and beta band (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). e LDA method cannot effectively extract these local features.…”

Section: Introductionmentioning

confidence: 99%

EEG Signal Classification Using Manifold Learning and Matrix‐Variate Gaussian Model

Zhu

Yang

et al. 2021

Computational Intelligence and Neuroscience

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In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.

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Sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) for feature extraction

Cited by 15 publications

References 38 publications

Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences

Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences

Application of Locally Invariant Robust PCA for Underwater Image Recognition

EEG Signal Classification Using Manifold Learning and Matrix‐Variate Gaussian Model

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