Paper Info
Title
Improved entropy of primitive for visual information estimation
Abstract
Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the sparse coefficient energy was ignored when calculating EoP, which may be critical in accounting for the primitive characteristics. To tackle this, an improved EoP is developed in this work via ℓ2 norm calculation. We further give mathematical derivations for its convergence verification. Experimental evaluations have also demonstrated that the improved EoP can achieve more stable convergence tendencies, which is consistent with the perceptual experiences. © 2016 IEEE.
Year
DOI
Venue
2016
10.1109/VCIP.2016.7805589
VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing
Keywords
Field
DocType
Entropy of primitive,orthogonal matching pursuit,sparse representation,visual information estimation
Convergence (routing),Matching pursuit,Computer vision,Human visual system model,Neural coding,Computer science,Sparse approximation,Artificial intelligence,Norm (mathematics),Perception,Statistical analysis
Conference
ISBN
Citations 
PageRank 
9781509053162
0
0.34
References 
Authors
10
7
Name
Order
Citations
PageRank
Wang Shurun101.35
Zhao Zhenghui2274.00
Xiang Zhang38812.61
Jian Zhang430426.09
Shiqi Wang51281120.37
Siwei Ma62229203.42
Wen Gao711374741.77