Effects of Retinal Distance and Feature Similarity on Disparity Coding

1973 ◽  
Author(s):  
W. L. Gulick ◽  
W. M. Youngs ◽  
John P. Galla
Keyword(s):  
Feature Similarity

scholarly journals Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 403
Author(s):  
Xun Zhang ◽  
Lanyan Yang ◽  
Bin Zhang ◽  
Ying Liu ◽  
Dong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Structured Data ◽  
Similarity Matrix ◽  
Scale Feature ◽  
Multi Scale ◽  
Adjacent Node ◽  
Feature Similarity ◽  
The Mean ◽  
Graph Neural Networks

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.

scholarly journals Video quality assessment using motion-compensated temporal filtering and manifold feature similarity

PLoS ONE ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. e0175798
Author(s):  
Yang Song ◽  
Mei Yu ◽  
Gangyi Jiang ◽  
Feng Shao ◽  
Zongju Peng
Keyword(s):  
Quality Assessment ◽  
Video Quality ◽  
Video Quality Assessment ◽  
Temporal Filtering ◽  
Feature Similarity
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