For most sparsecoding methods, data samples are first encoded as hand-crafted features, followed by another separate learning step that generates dictionary and sparse codes. However, such feature representations may...
详细信息
For most sparsecoding methods, data samples are first encoded as hand-crafted features, followed by another separate learning step that generates dictionary and sparse codes. However, such feature representations may not be optimally compatible with the learning process, thus producing suboptimal results. In this paper, we propose a new architecture for non-linear dictionary learning with sparsecoding, in which samples are mapped into sparse codes via carefully designed stacked auto-encoder (SAE) networks. We jointly learn a low-dimensional embedding of the data samples by means of an SAE and a dictionary in the low-dimensional space. Further, to leverage the prior knowledge, we develop a kernel regularized nonlinear dictionary learning method, which effectively incorporates the knowledge provided by the hand-crafted kernel. An iterative algorithm is developed to jointly search the solutions of the associated optimization problem and extensive experimental validations are performed to show that the proposed kernel regularized dictionary learning method achieves satisfactory performance.
In this paper, we study one-shot learning gesture recognition on RGB-D data recorded from Microsoft's Kinect. To this end, we propose a novel bag of manifold words (BoMW)based feature representation on symmetric p...
详细信息
In this paper, we study one-shot learning gesture recognition on RGB-D data recorded from Microsoft's Kinect. To this end, we propose a novel bag of manifold words (BoMW)based feature representation on symmetric positive definite (SPD) manifolds. In particular, we use covariance matrices to extract local features from RGB-D data due to its compact representation ability as well as the convenience of fusing both RGB and depth information. Since covariance matrices are SPD matrices and the space spanned by them is the SPD manifold, traditional learning methods in the Euclidean space, such as sparsecoding, cannot be directly applied to them. To overcome this problem, we propose a unified framework to transfer the sparsecoding on SPD manifolds to the one on the Euclidean space, which enables any existing learning method to be used. After building BoMW representation on a video from each gesture class, a nearest neighbor classifier is adopted to perform the one-shot learning gesture recognition. Experimental results on the ChaLearn gesture data set demonstrate the outstanding performance of the proposed one-shot learning gesture recognition method compared against the state-of-the-art methods. The effectiveness of the proposed feature extraction method is also validated on a new RGB-D action recognition data set.
The problem of place recognition is central to robot navigation. The robot needs to be able to recognize or at least to be able to estimate the likelihood that it has been at a place before when it has returned to a p...
详细信息
ISBN:
(纸本)9781509006199
The problem of place recognition is central to robot navigation. The robot needs to be able to recognize or at least to be able to estimate the likelihood that it has been at a place before when it has returned to a previously visited place. We cast the place recognition problem as one of classifying among multiple linear regression models, and argue that new theory from sparse signal representation offers the key to addressing the problem. In this paper, a joint kernel sparse coding model is developed to tackle the multivariate sonar samples place recognition problem. The experimental results show that the joint sparsecoding achieves better performance than 1-Nearest Neighborhood (1-NN) method.
The symmetric positive-definite (SPD) matrix, as a connected Riemannian manifold, has become increasingly popular for encoding image information. Most existing sparse models are still primarily developed in the Euclid...
详细信息
The symmetric positive-definite (SPD) matrix, as a connected Riemannian manifold, has become increasingly popular for encoding image information. Most existing sparse models are still primarily developed in the Euclidean space. They do not consider the non-linear geometrical structure of the data space, and thus are not directly applicable to the Riemannian manifold. In this paper, we propose a novel sparse representation method of SPD matrices in the data-dependent manifold kernel space. The graph Laplacian is incorporated into the kernel space to better reflect the underlying geometry of SPD matrices. Under the proposed framework, we design two different positive definite kernel functions that can be readily transformed to the corresponding manifold kernels. The sparse representation obtained has more discriminating power. Extensive experimental results demonstrate good performance of manifold kernelsparse codes in image classification, face recognition, and visual tracking.
暂无评论