Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsens...Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.展开更多
The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is tha...The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding(LULDE). The proposed approach can be seen as an extension of a local discriminant embedding(LDE)framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.展开更多
为了有效利用已标记与未标记样本提高高光谱遥感影像分类精度,提出一种新的半监督流形学习方法——半监督稀疏鉴别嵌入算法(SSDE)。该算法结合了近邻流形结构及稀疏性的优点,不仅保留样本间的稀疏重构关系,而且通过引入少量有标记的训...为了有效利用已标记与未标记样本提高高光谱遥感影像分类精度,提出一种新的半监督流形学习方法——半监督稀疏鉴别嵌入算法(SSDE)。该算法结合了近邻流形结构及稀疏性的优点,不仅保留样本间的稀疏重构关系,而且通过引入少量有标记的训练样本以及大量无标记训练样本来获得高维数据的内在属性以及低维流形结构,实现鉴别特征提取,提高分类精度。在Washington DC Mall和Indian Pine数据集上的分类识别实验表明,该算法能够较为有效地发现高维空间中数据的内蕴结构,分类性能比其他算法有明显的提升。在随机选取8个有类别标记和60个无类别标记的数据作为训练样本的情况下,本文提出的SSDE算法在上述两个数据集上的分类精度分别达到了77.36%和97.85%。展开更多
稀疏保持投影(SPP)是一种基于l1图的新型降维算法,它利用样本间的稀疏重构关系建图,但是SPP为非监督算法,分类效果受到限制。针对此问题,本文提出了一种新的稀疏流形学习算法-稀疏鉴别嵌入(SDE)。该算法在利用样本的稀疏重构关系建图时...稀疏保持投影(SPP)是一种基于l1图的新型降维算法,它利用样本间的稀疏重构关系建图,但是SPP为非监督算法,分类效果受到限制。针对此问题,本文提出了一种新的稀疏流形学习算法-稀疏鉴别嵌入(SDE)。该算法在利用样本的稀疏重构关系建图时引入了样本的类别信息,并通过优化目标函数来得到投影矩阵,使得不同类的数据点在低维嵌入空间中尽可能地分散开。SDE通过结合数据稀疏性及类间流形结构的优点,不仅保留样本间的稀疏重构关系,而且通过引入训练样本的类别信息实现稀疏鉴别特征提取,更有利于分类。在Urban和Washington DC Mall数据集上的实验结果表明:SDE算法比其他算法的分类性能有明显的提升,在每类随机选取16个训练样本的情况下,SDE算法的分类精度分别达到了73.47%和98.35%。展开更多
针对人脸识别中的非线性特征提取问题,提出一种基于核正交局部判别嵌入(KOLDE,kernel orthogonal local discriminant embedding)的人脸识别算法。首先通过引入基向量正交约束,得到OLDE算法,并给出算法的推导过程。然后为了更好地处理...针对人脸识别中的非线性特征提取问题,提出一种基于核正交局部判别嵌入(KOLDE,kernel orthogonal local discriminant embedding)的人脸识别算法。首先通过引入基向量正交约束,得到OLDE算法,并给出算法的推导过程。然后为了更好地处理高度复杂非线性结构数据,将OLDE向高维空间扩展,在核空间提取图像的高阶非线性信息,得到核空间OLDE算法。在ORL和PIE库上的人脸识别实验验证了算法的有效性。展开更多
邻域保持嵌入(Neighborhood Preserving Embedding,NPE),作为局部线性嵌入(Locally Linear Embedding,LLE)的线性化版本,由于在映射前后保持了数据的局部几何结构并得到了原始数据的子空间描述,在模式识别领域具有较强的应用价值。但作...邻域保持嵌入(Neighborhood Preserving Embedding,NPE),作为局部线性嵌入(Locally Linear Embedding,LLE)的线性化版本,由于在映射前后保持了数据的局部几何结构并得到了原始数据的子空间描述,在模式识别领域具有较强的应用价值。但作为非监督处理算法,在具体的模式分类中有一定局限性,提出一种NPE的改进算法——半监督判别邻域嵌入(SSDNE)算法,引入标记后样本点的类别信息,并在正则项中引入样本的流形结构,最大化标记样本点的类间信息和类内信息。既增加了算法的辨别能力又减少了监督算法中对样本点进行全标记的工作量。在ORL和YaleB人脸库上的实验结果表明,改进的算法较PCA、LDA、LPP以及原保持近邻判别嵌入算法的识别性能有了较明显的改善。展开更多
基金supported by a grant (2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation (NRF)funded by the Ministry of Education,Republic of Korea.
文摘Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.
基金Project supported by the National Natural Science Foundation of China(No.61402310)the Natural Science Foundation of Jiangsu Province,China(No.BK20141195)the State Key Laboratory for Novel Software Technology Foundation of Nanjing University,China(No.KFKT2014B11)
文摘The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding(LULDE). The proposed approach can be seen as an extension of a local discriminant embedding(LDE)framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.
文摘为了有效利用已标记与未标记样本提高高光谱遥感影像分类精度,提出一种新的半监督流形学习方法——半监督稀疏鉴别嵌入算法(SSDE)。该算法结合了近邻流形结构及稀疏性的优点,不仅保留样本间的稀疏重构关系,而且通过引入少量有标记的训练样本以及大量无标记训练样本来获得高维数据的内在属性以及低维流形结构,实现鉴别特征提取,提高分类精度。在Washington DC Mall和Indian Pine数据集上的分类识别实验表明,该算法能够较为有效地发现高维空间中数据的内蕴结构,分类性能比其他算法有明显的提升。在随机选取8个有类别标记和60个无类别标记的数据作为训练样本的情况下,本文提出的SSDE算法在上述两个数据集上的分类精度分别达到了77.36%和97.85%。
文摘稀疏保持投影(SPP)是一种基于l1图的新型降维算法,它利用样本间的稀疏重构关系建图,但是SPP为非监督算法,分类效果受到限制。针对此问题,本文提出了一种新的稀疏流形学习算法-稀疏鉴别嵌入(SDE)。该算法在利用样本的稀疏重构关系建图时引入了样本的类别信息,并通过优化目标函数来得到投影矩阵,使得不同类的数据点在低维嵌入空间中尽可能地分散开。SDE通过结合数据稀疏性及类间流形结构的优点,不仅保留样本间的稀疏重构关系,而且通过引入训练样本的类别信息实现稀疏鉴别特征提取,更有利于分类。在Urban和Washington DC Mall数据集上的实验结果表明:SDE算法比其他算法的分类性能有明显的提升,在每类随机选取16个训练样本的情况下,SDE算法的分类精度分别达到了73.47%和98.35%。
文摘针对人脸识别中的非线性特征提取问题,提出一种基于核正交局部判别嵌入(KOLDE,kernel orthogonal local discriminant embedding)的人脸识别算法。首先通过引入基向量正交约束,得到OLDE算法,并给出算法的推导过程。然后为了更好地处理高度复杂非线性结构数据,将OLDE向高维空间扩展,在核空间提取图像的高阶非线性信息,得到核空间OLDE算法。在ORL和PIE库上的人脸识别实验验证了算法的有效性。
文摘邻域保持嵌入(Neighborhood Preserving Embedding,NPE),作为局部线性嵌入(Locally Linear Embedding,LLE)的线性化版本,由于在映射前后保持了数据的局部几何结构并得到了原始数据的子空间描述,在模式识别领域具有较强的应用价值。但作为非监督处理算法,在具体的模式分类中有一定局限性,提出一种NPE的改进算法——半监督判别邻域嵌入(SSDNE)算法,引入标记后样本点的类别信息,并在正则项中引入样本的流形结构,最大化标记样本点的类间信息和类内信息。既增加了算法的辨别能力又减少了监督算法中对样本点进行全标记的工作量。在ORL和YaleB人脸库上的实验结果表明,改进的算法较PCA、LDA、LPP以及原保持近邻判别嵌入算法的识别性能有了较明显的改善。
