Analysis of shape alignment using Euclidean and Manhattan distance metrics

2017 ◽  
Author(s):  
Rohit Kumar
Keyword(s):  
Manhattan Distance ◽  
Distance Metrics ◽  
Shape Alignment

scholarly journals Optimization of deep learning features for age-invariant face recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 1833
Author(s):  
Amal A. Moustafa ◽  
Ahmed Elnakib ◽  
Nihal F. F. Areed
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
K Nearest Neighbor ◽  
Face Images ◽  
Knn Classifier ◽  
Deep Learning Features

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.

scholarly journals Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation

2021 ◽  
Vol 38 (6) ◽  
pp. 1843-1851
Author(s):  
Ouarda Soltani ◽  
Souad Benabdelkader
Keyword(s):  
Euclidean Distance ◽  
Image Database ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
Detection Rates ◽  
Skin Segmentation ◽  
University Of Texas ◽  
Important Means ◽  
Simulation Results ◽  
Skin Samples

The human color skin image database called SFA, specifically designed to assist research in the area of face recognition, constitutes a very important means particularly for the challenging task of skin detection. It has showed high performances comparing to other existing databases. SFA database provides multiple skin and non-skin samples, which in various combinations with each other allow creating new samples that could be useful and more effective. This particular aspect will be investigated, in the present paper, by creating four new representative skin samples according to the four rules of minimum, maximum, mean and median. The obtained samples will be exploited for the purpose of skin segmentation on the basis of the well-known Euclidean and Manhattan distance metrics. Thereafter, performances of the new representative skin samples versus performances of those skin samples, originally provided by SFA, will be illustrated. Simulation results in both SFA and UTD (University of Texas at Dallas) color face databases indicate that detection rates higher than 92% can be achieved with either measure.

scholarly journals Distance metric choice can both reduce and induce collinearity in geographically weighted regression

2018 ◽  
Vol 47 (3) ◽  
pp. 489-507 ◽  
Author(s):  
Alexis Comber ◽  
Khanh Chi ◽  
Man Q Huy ◽  
Quan Nguyen ◽  
Binbin Lu ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
House Price ◽  
Elastic Net ◽  
Weighted Regression ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
Distance Metric ◽  
Local Regression ◽  
Spatially Varying ◽  
The Impact

This paper explores the impact of different distance metrics on collinearity in local regression models such as geographically weighted regression. Using a case study of house price data collected in Hà Nội, Vietnam, and by fully varying both power and rotation parameters to create different Minkowski distances, the analysis shows that local collinearity can be both negatively and positively affected by distance metric choice. The Minkowski distance that maximised collinearity in a geographically weighted regression was approximate to a Manhattan distance with (power =  0.70) with a rotation of 30°, and that which minimised collinearity was parameterised with power  = 0.05 and a rotation of 70°. The results indicate that distance metric choice can provide a useful extra tuning component to address local collinearity issues in spatially varying coefficient modelling and that understanding the interaction of distance metric and collinearity can provide insight into the nature and structure of the data relationships. The discussion considers first, the exploration and selection of different distance metrics to minimise collinearity as an alternative to localised ridge regression, lasso and elastic net approaches. Second, it discusses the how distance metric choice could extend the methods that additionally optimise local model fit (lasso and elastic net) by selecting a distance metric that further helped minimise local collinearity. Third, it identifies the need to investigate the relationship between kernel bandwidth, distance metrics and collinearity as an area of further work.

scholarly journals A Study of Distance Metrics in Histogram Based Image Retrieval

2013 ◽  
Vol 4 (3) ◽  
pp. 821-830 ◽  
Author(s):  
Abhijeet Kumar Sinha ◽  
K.K. Shukla
Keyword(s):  
Image Retrieval ◽  
Digital Data ◽  
Content Based Image Retrieval ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
Color Spaces ◽  
Query Image ◽  
Histogram Intersection ◽  
Exhaustive Study ◽  
Different Color

There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.

Cloud-integrated wireless-optical broadband access network with survivability

2020 ◽  
Vol 10 ◽  
Author(s):  
Sangita Solanki ◽  
Raksha Upadhyay ◽  
Uma Rathore Bhatt
Keyword(s):  
Access Network ◽  
Access Networks ◽  
Manhattan Distance ◽  
Broadband Access ◽  
Optical Access Networks ◽  
Component Level ◽  
Backup Path ◽  
Wireless Router ◽  
Path Computation ◽  
Broadband Access Network

Cloud-integrated wireless optical broadband (CIW) access networks inheriting advantages of cloud computing, wireless and optical access networks have a broad prospect in the future. Due to failure of components like OLT level, ONU level, link or path failure and cloud component level in CIW, survivability is becoming one of the important issues. In this paper, we have presented cloud-integrated wireless-optical broadband access network with survivability using integer linear programming (ILP) model, to minimize the number of cloud components while providing maximum backup paths. Hence, we have proposed protection through cloud-integrated wireless router to available ONUs (PCIWRAO). So, evaluated the backup path computation. We have considered ONU level failure in which the affected traffic is transferred through wireless routers and cloud component to the available ONUs using Manhattan distance algorithm. Simulation results show different configurations for different number of routers and cloud components illustrating available backup path when ONU fails.

Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

2019 ◽  
Vol 12 ◽  
Author(s):  
Seema Rani ◽  
Avadhesh Kumar ◽  
Naresh Kumar
Keyword(s):  
Question Answering ◽  
Neural Model ◽  
Manhattan Distance ◽  
Semantic Matching ◽  
English Only ◽  
Detection Model ◽  
Proposed Model ◽  
Reported Study ◽  
Social Media Platforms ◽  
Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

scholarly journals Clustering electricity market participants via FRM models

2020 ◽  
pp. 1-12
Author(s):  
Ayla Gülcü ◽  
Sedrettin Çalişkan
Keyword(s):  
Electricity Market ◽  
Distance Metrics ◽  
Agglomerative Clustering ◽  
Timely Manner ◽  
Short Term ◽  
Clustering Techniques ◽  
Market Participants ◽  
Cosine Distance ◽  
Collateral Mechanism

Collateral mechanism in the Electricity Market ensures the payments are executed on a timely manner; thus maintains the continuous cash flow. In order to value collaterals, Takasbank, the authorized central settlement bank, creates segments of the market participants by considering their short-term and long-term debt/credit information arising from all market activities. In this study, the data regarding participants’ daily and monthly debt payment and penalty behaviors is analyzed with the aim of discovering high-risk participants that fail to clear their debts on-time frequently. Different clustering techniques along with different distance metrics are considered to obtain the best clustering. Moreover, data preprocessing techniques along with Recency, Frequency, Monetary Value (RFM) scoring have been used to determine the best representation of the data. The results show that Agglomerative Clustering with cosine distance achieves the best separated clustering when the non-normalized dataset is used; this is also acknowledged by a domain expert.

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