The Normalized Similarity Metric and Its Applications

2007 ◽  
Author(s):  
Shihyen Chen ◽  
Bin Ma ◽  
Kaizhong Zhang
Keyword(s):  
Similarity Metric

Automated image registration by maximization of a region similarity metric

1997 ◽  
Vol 8 (6) ◽  
pp. 513-518 ◽  
Author(s):  
Zhi-Pei Liang ◽  
Hao Pan ◽  
Richard L. Magin ◽  
Narendra Ahuja ◽  
Thomas S. Huang
Keyword(s):  
Image Registration ◽  
Similarity Metric ◽  
Automated Image Registration

Towards a knowledge-based probabilistic and context-aware social recommender system

2017 ◽  
Vol 44 (4) ◽  
pp. 464-490 ◽  
Author(s):  
Luis Omar Colombo-Mendoza ◽  
Rafael Valencia-García ◽  
Alejandro Rodríguez-González ◽  
Ricardo Colomo-Palacios ◽  
Giner Alor-Hernández
Keyword(s):  
Latent Dirichlet Allocation ◽  
Evaluation Method ◽  
Context Aware ◽  
Average Precision ◽  
Case Scenario ◽  
Similarity Metric ◽  
Knowledge Based ◽  
Context Modelling ◽  
Evaluation Metric ◽  
Partial Implementation

In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users’ preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).

scholarly journals Learning deep similarity metric for 3D MR–TRUS image registration

2018 ◽  
Vol 14 (3) ◽  
pp. 417-425 ◽  
Author(s):  
Grant Haskins ◽  
Jochen Kruecker ◽  
Uwe Kruger ◽  
Sheng Xu ◽  
Peter A. Pinto ◽  
...  
Keyword(s):  
Image Registration ◽  
Similarity Metric ◽  
Trus Image
Sign in / Sign up

Export Citation Format

Share Document