Two-Stage Filtering Scheme for Sparse Representation Based Interest Point Matching for Person Re-identification

2015 ◽  
pp. 345-356 ◽  
Author(s):  
Mohamed Ibn Khedher ◽  
Mounim A. El Yacoubi
Keyword(s):  
Sparse Representation ◽  
Interest Point ◽  
Two Stage ◽  
Point Matching ◽  
Interest Point Matching

Evaluation of Interest Point Matching Methods for Projective Reconstruction of 3D Scenes

2016 ◽  
Vol 14 (3) ◽  
pp. 1393-1400 ◽  
Author(s):  
D.N. Brito ◽  
C.F.G. Nunes ◽  
F.L.C. Padua ◽  
A. Lacerda
Keyword(s):  
Interest Point ◽  
Projective Reconstruction ◽  
Point Matching ◽  
Matching Methods ◽  
Interest Point Matching

scholarly journals Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning

2007 ◽  
Vol 9 (5) ◽  
pp. 1037-1048 ◽  
Author(s):  
Wan-Lei Zhao ◽  
Chong-Wah Ngo ◽  
Hung-Khoon Tan ◽  
Xiao Wu
Keyword(s):  
Pattern Learning ◽  
Interest Point ◽  
Point Matching ◽  
Interest Point Matching

Analysis of Sparse Representation and Blind Source Separation

2004 ◽  
Vol 16 (6) ◽  
pp. 1193-1234 ◽  
Author(s):  
Yuanqing Li ◽  
Andrzej Cichocki ◽  
Shun-ichi Amari
Keyword(s):  
Sparse Representation ◽  
Blind Source Separation ◽  
Clustering Algorithm ◽  
Source Separation ◽  
Coefficient Matrix ◽  
Data Matrix ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Basis Matrix ◽  
Two Stage

In this letter, we analyze a two-stage cluster-then-l1-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l1 norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l1—norm solution and the l0—norm solution is also analyzed according to a probabilistic framework. If the obtained l1—norm solution is sufficiently sparse, then it is equal to the l0—norm solution with a high probability. Furthermore, the l1—norm solution is robust to noise, but the l0—norm solution is not, showing that the l1—norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed

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