Low-rank 2D Local Discriminant Graph Embedding for Robust Image Feature Extraction
Low-rank 2D Local Discriminant Graph Embedding for Robust Image Feature Extraction
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用于鲁棒图像特征提取的低秩二维局部判别图嵌入
DOI：
10.1016/j.patcog.2022.109034
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发表时间：
2022-09
影响因子：
8
通讯作者：
Hao Zheng
中科院分区：
文献类型：
--
作者：
Minghua Wan;Xueyu Chen;Tianming Zhan;Guowei Yang;Hai Tan;Hao Zheng
论文的主要贡献总结如下： 学习了一种基于图嵌入的低秩矩阵，可以同时进行子空间学习、图拉普拉斯正则化和低秩学习的统一策略，并提供了凸优化问题的迭代解决方案； • 将图嵌入框架与低秩矩阵相结合，提出两种类内和类间加权矩阵图，充分发现邻域的流形结构信息，提高二维图像的识别能力。 • 建议确保给定数据分为低秩特征编码部分和稀疏噪声误差部分以提高识别能力，这可以在学习最优投影时减弱噪声和遮挡的影响。二维局部保留投影（2DLPP）算法作为一种流行的特征提取算法，已在许多领域得到成功应用。 2DLPP算法利用2D图像表示，保留了流形属性并保留了高维空间数据的局部信息。然而，2DLPP算法在实际应用中可能会遇到一些问题，例如缺乏判别能力、奇异性问题以及对数据中的遮挡和噪声敏感等。因此，本文将低秩引入2DLPP算法中，并提出一种新的特征提取算法，即低秩二维局部判别图嵌入（LR-2DLDGE）来解决这些问题。为了提高 LR-2DLDGE 算法的鲁棒性，我们融合了图嵌入中的判别信息和数据的低秩属性。该算法具有三个优点：首先，该算法使用图嵌入（GE）框架来维护数据之间的局部邻域判别信息。其次，LR-2DLDGE算法确保数据点尽可能独立于特征空间中的不同类。最后，算法使用L 1 范数作为约束，通过低秩学习减少噪声和腐败的影响。阐述并证明了该算法的理论计算复杂度和收敛性。对三个遮挡和噪声图像数据集的广泛实验结果分别证实了 LR-2DLDGE 的有效性和鲁棒性。
• The main contributions of the paper are summarized as follows: • It is learned a low-rank matrix based on the graph embedding that can simultaneously perform subspace learning, graph Laplacian regularization, and low-rank learning in a unified strategy is proposed and an iterative solution to the convex optimization problem is provided; • It is combined the graph embedding framework with the low-rank matrix, two intraclass and interclass weighted matrix graphs are proposed, which fully discover the manifold structural information of the neighbourhood and improve the recognition ability in 2D images; • It is proposed to ensure that the given data are divided into a low-rank feature coding part and a sparse noise error part to improve the recognition ability, which can weaken the influence of noise and occlusion when learning the optimal projection. As a popular feature extraction algorithm, the 2D local preserving projections (2DLPP) algorithm has been successfully applied in many fields. Using 2D image representation, the 2DLPP algorithm preserves the manifold attributes and retains the local information of high-dimensional space data. However, the 2DLPP algorithm may encounter some problems in real-world applications, such as a lack of discriminatory ability, singularity problems, and sensitivity to occlusion and noise in data. Therefore, this paper introduces low-rank into the 2DLPP algorithm and proposes a new feature extraction algorithm, which is the low-rank two-dimensional local discriminant graph embedding (LR-2DLDGE), to solve these problems. To improve the LR-2DLDGE algorithm robustness, we fuse the discriminant information in graph embedding and the low-rank properties of the data. The algorithm has three advantages: First, the algorithm uses a graph embedding (GE) framework to maintain the local neighbourhood discrimination information between data. Second, the LR-2DLDGE algorithm ensures that the data points are as independent as possible from different classes in the feature space. Finally, the algorithm uses the L 1 -norm as a constraint and reduces the influence of noise and corruption through low-rank learning. The theoretical computational complexity and convergence of the algorithm are explicated and proved. Extensive experimental results on three occluded and noisy image datasets confirm the effectively and robustness of LR-2DLDGE, respectively.
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