Extracting an image similarity index using meta-data content for image mining applications

1997 ◽  
Author(s):  
Srinivasan Raghavan ◽  
Robert F. Cromp ◽  
Sridhar Srinivasan ◽  
Raadhakrishnan Poovendran ◽  
William J. Campbell ◽  
...  
Keyword(s):  
Similarity Index ◽  
Image Similarity ◽  
Image Mining ◽  
Meta Data ◽  
Data Content

Combined image similarity index

2012 ◽  
Vol 19 (5) ◽  
pp. 349-354 ◽  
Author(s):  
Krzysztof Okarma
Keyword(s):  
Similarity Index ◽  
Image Similarity

scholarly journals Discrete Protein Metric (DPM): A new image similarity metric to calculate accuracy of deep learning-generated cell focal adhesion predictions

2021 ◽  
Author(s):  
Miguel Alejandro Contreras ◽  
William Bachman ◽  
David S Long
Keyword(s):  
Deep Learning ◽  
Focal Adhesion ◽  
Pearson Correlation ◽  
Similarity Index ◽  
Ground Truth ◽  
Image Similarity ◽  
Point Pattern ◽  
Accuracy Score ◽  
Site Location ◽  
Similarity Metric

Understanding cell behaviors can provide new knowledge on the development of different pathologies. Focal adhesion (FA) sites are important sub-cellular structures that are involved in these processes. To better facilitate the study of FA sites, deep learning (DL) can be used to predict FA site morphology based on limited datasets (e.g., cell membrane images). However, calculating the accuracy score of these predictions can be challenging due to the discrete/point pattern like nature of FA sites. In the present work, a new image similarity metric, discrete protein metric (DPM), was developed to calculate FA prediction accuracy. This metric measures differences in distribution (d), shape/size (s), and angle (a) of FA sites between the predicted image and its ground truth image. Performance of the DPM was evaluated by comparing it to three other commonly used image similarity metrics: Pearson correlation coefficient (PCC), feature similarity index (FSIM), and Intersection over Union (IoU). A sensitivity analysis was performed by comparing changes in each metric value due to quantifiable changes in FA site location, number, aspect ratio, area, or orientation. Furthermore, accuracy score of DL-generated predictions was calculated using all four metrics to compare their ability to capture variation across samples. Results showed better sensitivity and range of variation for DPM compared to the other metrics tested. Most importantly, DPM had the ability to determine which FA predictions were quantitatively more accurate and consistent with qualitative assessments. The proposed DPM hence provides a method to validate DL-generated FA predictions and can be extended to evaluating other predicted or segmented discrete structures of biomedical relevance.

scholarly journals Modify Manhattan Distance for Image Similarity

2020 ◽  
Vol 2 (4) ◽  
pp. 12-16
Author(s):  
Tasaddi Maalak Hanoun ◽  
Kadhim M. Hashim
Keyword(s):  
Statistical Approach ◽  
Similarity Index ◽  
Structural Similarity ◽  
Image Similarity ◽  
Manhattan Distance ◽  
The Novel ◽  
Structural Similarity Index ◽  
Significant Difference ◽  
Index Measure ◽  
Similarity Method

A New measure is proposed for assessing the similarity among gray-scale images. The well-known Structural Similarity Index Measure (SSIM) has been designed using a statistical approach that fails under significant noise (lowPSNR). The proposed measure, denoted by Manhattan distance and STD, uses a combination of two parts: the first part is the Geometric method, while the second part is based on the statistical feature. The concept of manhattan distance is used in the geometric part. The new measure shows the advantages of statistical approaches and geometric approaches. The proposed similarity method is an outcome for the human face. The novel measure outperforms the classical SSIM in detecting image similarity at low PSNR, with a significant difference in performance. AMS subject classification:

Complex Wavelet Structural Similarity: A New Image Similarity Index

2009 ◽  
Vol 18 (11) ◽  
pp. 2385-2401 ◽  
Author(s):  
M.P. Sampat ◽  
Zhou Wang ◽  
S. Gupta ◽  
A.C. Bovik ◽  
M.K. Markey
Keyword(s):  
Similarity Index ◽  
Structural Similarity ◽  
Image Similarity ◽  
Complex Wavelet

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 Pothole Region Extraction Based On Similarity Evaluation Scale Classification Using Image Processing

2021 ◽  
Vol 12 (6) ◽  
pp. 871-877
Author(s):  
Min-Jeong Kim Et.al
Keyword(s):  
Image Processing ◽  
Global Warming ◽  
Similarity Index ◽  
Structural Similarity ◽  
Image Similarity ◽  
Structural Similarity Index ◽  
Pavement Deterioration ◽  
Region Extraction ◽  
Index Measure ◽  
Scale Classification

Pavement deterioration and abnormal climate induced by global warming lead to a constant rise in the number of potholes. Accordingly, the loss cost for maintenance and accidents also increases. Therefore, it is necessary to develop a method of classifying pavement potholes and detecting their locations. This study proposes the pothole region extraction based on similarity evaluation scale classification using image processing. The proposed technique sets up a classification threshold appropriately by considering the structure, brightness, and other factors of the grayscale-converted image through SSIM (Structural Similarity Index Measure). It binarizes porthole images classified according to the threshold, and then extracts pothole regions through the threshold based segmentation. A conventional image classification method utilizes the rules found in objects or the label selected by a user. The proposed method can take into account detailed factors by comparing image similarity in the unit of pixel. According to the performance evaluation, the proposed classification method’s F1-score is 0.83, and its accuracy of pothole region extraction is 0.851. Therefore, with the proposed technique, it is possible to make classification in consideration of similarity between images. In addition, the proposed method makes it possible to detect the regions similar to actual potholes accurately.

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