An image similarity measure based on joint histogram — Entropy for face recognition

2017 ◽  
Author(s):  
Mohammed Abdulameer Aljanabi ◽  
Noor Abdalrazak Shnain ◽  
Song Feng Lu
Keyword(s):  
Face Recognition ◽  
Similarity Measure ◽  
Image Similarity ◽  
Joint Histogram

scholarly journals Structural Geodesic-Tchebychev Transform: An Image Similarity Measure for Face Recognition

2016 ◽  
Vol 12 (9) ◽  
pp. 464-470
Author(s):  
Ali Nadhim Razzaq ◽  
Zahir M. Hussain ◽  
Hind Rustum Mohammed
Keyword(s):  
Face Recognition ◽  
Similarity Measure ◽  
Image Similarity

scholarly journals An Entropy-Histogram Approach for Image Similarity and Face Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammed Abdulameer Aljanabi ◽  
Zahir M. Hussain ◽  
Song Feng Lu
Keyword(s):  
Information Theory ◽  
Face Recognition ◽  
Image Recognition ◽  
Similarity Index ◽  
Similarity Measures ◽  
Image Similarity ◽  
Information Theoretic ◽  
Joint Histogram ◽  
The Face ◽  
Index Measure

Image similarity and image recognition are modern and rapidly growing technologies because of their wide use in the field of digital image processing. It is possible to recognize the face image of a specific person by finding the similarity between the images of the same person face and this is what we will address in detail in this paper. In this paper, we designed two new measures for image similarity and image recognition simultaneously. The proposed measures are based mainly on a combination of information theory and joint histogram. Information theory has a high capability to predict the relationship between image intensity values. The joint histogram is based mainly on selecting a set of local pixel features to construct a multidimensional histogram. The proposed approach incorporates the concepts of entropy and a modified 1D version of the 2D joint histogram of the two images under test. Two entropy measures were considered, Shannon and Renyi, giving a rise to two joint histogram-based, information-theoretic similarity measures: SHS and RSM. The proposed methods have been tested against powerful Zernike-moments approach with Euclidean and Minkowski distance metrics for image recognition and well-known statistical approaches for image similarity such as structural similarity index measure (SSIM), feature similarity index measure (FSIM) and feature-based structural measure (FSM). A comparison with a recent information-theoretic measure (ISSIM) has also been considered. A measure of recognition confidence is introduced in this work based on similarity distance between the best match and the second-best match in the face database during the face recognition process. Simulation results using AT&T and FEI face databases show that the proposed approaches outperform existing image recognition methods in terms of recognition confidence. TID2008 and IVC image databases show that SHS and RSM outperform existing similarity methods in terms of similarity confidence.

scholarly journals Hausdorff distance-based multiresolution maps applied to image similarity measure

2007 ◽  
Vol 55 (3) ◽  
pp. 164-174 ◽  
Author(s):  
E Baudrier ◽  
G Millon ◽  
F Nicolier ◽  
R Seulin ◽  
S Ruan
Keyword(s):  
Similarity Measure ◽  
Hausdorff Distance ◽  
Image Similarity

scholarly journals New Image Similarity Measure for Bronchoscope Tracking Based on Image Registration

2003 ◽  
pp. 399-406 ◽  
Author(s):  
Daisuke Deguchi ◽  
Kensaku Mori ◽  
Yasuhito Suenaga ◽  
Jun-ichi Hasegawa ◽  
Jun-ichiro Toriwaki ◽  
...  
Keyword(s):  
Image Registration ◽  
Similarity Measure ◽  
Image Similarity
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