Chia, Sparse representation with kernels, IEEE Trans. Image Process. 22 (2013) 423-434.S. Gao, I. W. Tsang, and L. Chia. Sparse representation with kernels. IEEE Trans. Image Process., 22(2):423-434, Feb 2013.S. Gao, I.W.-H. Tsang, L.-T. Chia, Sparse representation with ...
Kernel sparse representation based classification3. LBP based kernels3.1. Region-level kernels3.2. Pixel-level kernel4. Experiments4.1. Database4.2. With ... C Kang,S Liao,S Xiang,... - 《Neurocomputing》 被引量: 14发表: 2014年 Multiple Kernel Sparse Representation for Airborne LiDAR Data Cla...
We tackle these problems by leveraging a novel integral representation of functions in RKHSs that allows for arbitrary centers and different kernels at each center. To address the complexity issues, we then write the function estimation problem as a sparse functional program that explicitly minimizes ...
These kernels characterize the geodesic distance and can be computed efficiently. We also consider the geometric structure in the DL process by updating atom matrices in the Riemannian space instead of in the Euclidean space. The proposed method is evaluated with various vision problems and shows ...
Different kernels cause various class discriminations owing to their different geometrical structures of the data in the feature space. In this paper, a method of kernel optimization by maximizing a measure of class separability in the empirical feature space with sparse representation-based classifier ...
Kernel sparse representation with pixel-level and region-level local feature kernels for face recognition AbstractFace recognition has been popular in the pattern recognition field for decades, but it is still a difficult problem due to the various image distor... C Kang,S Liao,S Xiang,... -...
Optimally sparse representation in general (nonorthogonal) dictionaries via L1 minimization [J]. PNAS, 2003, 100(5): 2197-2202 7 [Wipf_NIPS2011] D.P. Wipf. Sparse estimation with structured dictionaries [C]. Advances in Neural Information Processing Systems 24, 2011: 2016-2024 [Wipf2010] D....
Sparse signal representation refers to decomposition of signals using just a few basis functions, also known as kernels or atoms, and has been the topic of a wide spectrum of researches. It has application in many areas such as 1. image denoising and restoration, 2. signal sampling and recove...
In sparse coding it is common to tile an image into nonoverlapping patches, and then use a dictionary to create a sparse representation of each tile independently. In this situation, the overcompleteness of the dictionary is the number of dictionary elements divided by the patch size. In deconvo...
To address this problem, we present a novel adaptive multikernel sparse representation (AMSR) method. First, multiple basic kernel functions are used to map all training samples into high-dimensional Hilbert space, which captures the nonlinear feature similarities of different tactile samples, and ...