scholarly journals Matchability Prediction for Full-Search Template Matching Algorithms

2015 ◽  
Author(s):  
Adrian Penate-Sanchez ◽  
Lorenzo Porzi ◽  
Francesc Moreno-Noguer
Keyword(s):  
Template Matching ◽  
Search Template ◽  
Full Search

A Novel Template Matching Scheme for Fast Full-Search Boosted by an Integral Image

2010 ◽  
Vol 17 (1) ◽  
pp. 107-110 ◽  
Author(s):  
Jik-Han Jung ◽  
Hwal-Suk Lee ◽  
Je Hee Lee ◽  
Dong-Jo Park
Keyword(s):  
Template Matching ◽  
Integral Image ◽  
Full Search

Fast Full-Search Equivalent Template Matching by Enhanced Bounded Correlation

2008 ◽  
Vol 17 (4) ◽  
pp. 528-538 ◽  
Author(s):  
Stefano Mattoccia ◽  
Federico Tombari ◽  
Luigi Di Stefano
Keyword(s):  
Template Matching ◽  
Full Search

Ensemble Visual Content based Search and Retrieval for Natural Scene Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Template Matching ◽  
Feature Descriptor ◽  
Natural Scene ◽  
Visual Content ◽  
Other Information ◽  
Similar Images ◽  
State Of Art ◽  
Natural Scene Images ◽  
Hybrid Methodology ◽  
Search And Retrieval

Content based image retrieval (CBIR) is one of the field for information retrieval where similar images are retrieved from database based on the various image descriptive parameters. The image descriptor vector is used by machine learning based systems to store, learn and template matching. These feature descriptor vectors locally or globally demonstrate the visual content present in an image using texture, color, shape, and other information. In past, several algorithms were proposed to fetch the variety of contents from an image based on which the image is retrieved from database. But, the literature suggests that the precision and recall for the gained results using single content descriptor is not significant. The main vision of this paper is to categorize and evaluate those algorithms, which were proposed in the interval of last 10 years. In addition, experiment is performed using a hybrid content descriptors methodology that helps to gain the significant results as compared with state-of-art algorithms. The hybrid methodology decreases the error rate and improves the precision and recall for large natural scene images dataset having more than 20 classes.

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