scholarly journals Image Clustering Based on Multi-Scale Deep Maximize Mutual Information and Self-Training Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 160285-160296
Author(s):  
Siquan Yu ◽  
Guiping Shen ◽  
Peiyao Wang ◽  
Yuning Wang ◽  
Chengdong Wu
Keyword(s):  
Mutual Information ◽  
Image Clustering ◽  
Training Algorithm ◽  
Multi Scale

scholarly journals Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 118 ◽  
Author(s):  
Yudan Liu ◽  
Xiaomin Yang ◽  
Rongzhu Zhang ◽  
Marcelo Keese Albertini ◽  
Turgay Celik ◽  
...  
Keyword(s):  
Mutual Information ◽  
Image Fusion ◽  
Sparse Representation ◽  
Initial Weight ◽  
Edge Preservation ◽  
Multi Scale ◽  
Joint Sparse Representation ◽  
Final Weight ◽  
Fused Image ◽  
Correlation Information

Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information ( M I )—5.3377, feature mutual information ( F M I )—0.5600, normalized weighted edge preservation value ( Q A B / F )—0.6978 and nonlinear correlation information entropy ( N C I E )—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification.

Motion Estimation Based on Mutual Information and Adaptive Multi-Scale Thresholding

2016 ◽  
Vol 25 (3) ◽  
pp. 1095-1108 ◽  
Author(s):  
Rui Xu ◽  
David Taubman ◽  
Aous Thabit Naman
Keyword(s):  
Mutual Information ◽  
Motion Estimation ◽  
Multi Scale

scholarly journals Interpretation of multi-scale permeability data through an information theory perspective

2020 ◽  
Vol 24 (6) ◽  
pp. 3097-3109
Author(s):  
Aronne Dell'Oca ◽  
Alberto Guadagnini ◽  
Monica Riva
Keyword(s):  
Information Theory ◽  
Mutual Information ◽  
Information Content ◽  
Gas Permeability ◽  
Measurement Scale ◽  
Measurement Scales ◽  
Multi Scale ◽  
Multivariate Mutual Information ◽  
Permeability Data

Abstract. We employ elements of information theory to quantify (i) the information content related to data collected at given measurement scales within the same porous medium domain and (ii) the relationships among information contents of datasets associated with differing scales. We focus on gas permeability data collected over Berea Sandstone and Topopah Spring Tuff blocks, considering four measurement scales. We quantify the way information is shared across these scales through (i) the Shannon entropy of the data associated with each support scale, (ii) mutual information shared between data taken at increasing support scales, and (iii) multivariate mutual information shared within triplets of datasets, each associated with a given scale. We also assess the level of uniqueness, redundancy and synergy (rendering, i.e., information partitioning) of information content that the data associated with the intermediate and largest scales provide with respect to the information embedded in the data collected at the smallest support scale in a triplet. Highlights. Information theory allows characterization of the information content of permeability data related to differing measurement scales. An increase in the measurement scale is associated with quantifiable loss of information about permeability. Redundant, unique and synergetic contributions of information are evaluated for triplets of permeability datasets, each taken at a given scale.

Video tamper detection based on multi-scale mutual information

2017 ◽  
Vol 78 (19) ◽  
pp. 27109-27126 ◽  
Author(s):  
Wei Wei ◽  
Xunli Fan ◽  
Houbing Song ◽  
Huihui Wang
Keyword(s):  
Mutual Information ◽  
Tamper Detection ◽  
Multi Scale

scholarly journals Finding informative neurons in the brain using Multi-Scale Relevance

2018 ◽  
Author(s):  
Ryan John Cubero ◽  
Matteo Marsili ◽  
Yasser Roudi
Keyword(s):  
Mutual Information ◽  
Tuning Curve ◽  
A Priori ◽  
Electrode Array ◽  
Firing Activity ◽  
Significant Information ◽  
Multi Scale ◽  
Time Stamps ◽  
Wide Variability ◽  
The Brain

AbstractWe propose a metric – called Multi-Scale Relevance (MSR) – to score neurons for their prominence in encoding for the animal’s behaviour that is being observed in a multi-electrode array recording experiment. The MSR assumes that relevant neurons exhibit a wide variability in their dynamical state, in response to the external stimulus, across different time scales. It is a non-parametric, fully featureless indicator, in that it uses only the time stamps of the firing activity, without resorting to any a priori covariate or invoking any specific tuning curve for neural activity. We test the method on data from freely moving rodents, where we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded.

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