In pattern recognition,the task of image set classification has often been performed by representing data using symmetric positive definite(SPD)matrices,in conjunction with the metric of the resulting Riemannian manif...In pattern recognition,the task of image set classification has often been performed by representing data using symmetric positive definite(SPD)matrices,in conjunction with the metric of the resulting Riemannian manifold.In this paper,we propose a new data representation framework for image sets which we call component symmetric positive definite representation(CSPD).Firstly,we obtain sub-image sets by dividing the images in the set into square blocks of the same size,and use a traditional SPD model to describe them.Then,we use the Riemannian kernel to determine similarities of corresponding subimage sets.Finally,the CSPD matrix appears in the form of the kernel matrix for all the sub-image sets;its i,j-th entry measures the similarity between the i-th and j-th sub-image sets.The Riemannian kernel is shown to satisfy Mercer’s theorem,so the CSPD matrix is symmetric and positive definite,and also lies on a Riemannian manifold.Test on three benchmark datasets shows that CSPD is both lower-dimensional and more discriminative data descriptor than standard SPD for the task of image set classification.展开更多
特征提取算法中利用样本间的协同表示关系构造邻接图只考虑所有训练样本的协同能力,而忽视了每一类训练样本的内在竞争能力。为此,本文提出一种基于竞争性协同表示的局部判别投影特征提取算法(competitivecollaborative repesentation-b...特征提取算法中利用样本间的协同表示关系构造邻接图只考虑所有训练样本的协同能力,而忽视了每一类训练样本的内在竞争能力。为此,本文提出一种基于竞争性协同表示的局部判别投影特征提取算法(competitivecollaborative repesentation-based local discrininant projection for feature extraction,CCRLDP),该算法利用基于具有竞争性协同表示的方法构造类间图和类内图,考虑到邻接图中各类型系数的影响,引入保留正表示系数的思想稀疏化邻接图,通过计算类内散度矩阵和类间散度矩阵来刻画图像的局部结构并得其最优投影矩阵。在一些数据集上的实验结果表明,相比同类基于局部判别投影的特征提取算法,该算法具有很高的识别率,并在噪声和遮挡上具有良好的鲁棒性,该算法能有效地提高图像的识别效率。展开更多
文摘In pattern recognition,the task of image set classification has often been performed by representing data using symmetric positive definite(SPD)matrices,in conjunction with the metric of the resulting Riemannian manifold.In this paper,we propose a new data representation framework for image sets which we call component symmetric positive definite representation(CSPD).Firstly,we obtain sub-image sets by dividing the images in the set into square blocks of the same size,and use a traditional SPD model to describe them.Then,we use the Riemannian kernel to determine similarities of corresponding subimage sets.Finally,the CSPD matrix appears in the form of the kernel matrix for all the sub-image sets;its i,j-th entry measures the similarity between the i-th and j-th sub-image sets.The Riemannian kernel is shown to satisfy Mercer’s theorem,so the CSPD matrix is symmetric and positive definite,and also lies on a Riemannian manifold.Test on three benchmark datasets shows that CSPD is both lower-dimensional and more discriminative data descriptor than standard SPD for the task of image set classification.
文摘特征提取算法中利用样本间的协同表示关系构造邻接图只考虑所有训练样本的协同能力,而忽视了每一类训练样本的内在竞争能力。为此,本文提出一种基于竞争性协同表示的局部判别投影特征提取算法(competitivecollaborative repesentation-based local discrininant projection for feature extraction,CCRLDP),该算法利用基于具有竞争性协同表示的方法构造类间图和类内图,考虑到邻接图中各类型系数的影响,引入保留正表示系数的思想稀疏化邻接图,通过计算类内散度矩阵和类间散度矩阵来刻画图像的局部结构并得其最优投影矩阵。在一些数据集上的实验结果表明,相比同类基于局部判别投影的特征提取算法,该算法具有很高的识别率,并在噪声和遮挡上具有良好的鲁棒性,该算法能有效地提高图像的识别效率。