Evaluation the influence of distance-based K-means method for detecting moving vehicles

Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.

Diurnal variation in the human skin microbiome affects accuracy of forensic microbiome matching

Background The human skin microbiome has been recently investigated as a potential forensic tool, as people leave traces of their potentially unique microbiomes on objects and surfaces with which they interact. In this metagenomic study of four people in Hong Kong, their homes, and public surfaces in their neighbourhoods, we investigated the stability and identifiability of these microbiota traces on a timescale of hours to days. Results Using a Canberra distance-based method of comparing skin and surface microbiomes, we found that a person could be accurately matched to their household in 84% of tests and to their neighbourhood in 50% of tests, and that matching accuracy did not decay for household surfaces over the 10-day study period, although it did for public surfaces. The time of day at which a skin or surface sample was taken affected matching accuracy, and 160 species across all sites were found to have a significant variation in abundance between morning and evening samples. We hypothesised that daily routines drive a rhythm of daytime dispersal from the pooled public surface microbiome followed by normalisation of a person’s microbiome by contact with their household microbial reservoir, and Dynamic Bayesian Networks (DBNs) supported dispersal from public surfaces to skin as the major dispersal route among all sites studied. Conclusions These results suggest that in addition to considering the decay of microbiota traces with time, diurnal patterns in microbiome exposure that contribute to the human skin microbiome assemblage must also be considered in developing this as a potential forensic method.

An efficient distance estimation and centroid selection based on k-means clustering for small and large dataset

In this paper an efficient distance estimation and centroid selection based on k-means clustering for small and large dataset. Data pre-processing was performed first on the dataset. For the complete study and analysis PIMA Indian diabetes dataset was considered. After pre-processing distance and centroid estimation was performed. It includes initial selection based on randomization and then centroids updations were performed till the iterations or epochs determined. Distance measures used here are Euclidean distance (Ed), Pearson Coefficient distance (PCd), Chebyshev distance (Csd) and Canberra distance (Cad). The results indicate that all the distance algorithms performed approximately well in case of clustering but in terms of time Cad outperforms in comparison to other algorithms.

A Novel Regret Theory-Based Decision-Making Method Combined with the Intuitionistic Fuzzy Canberra Distance

In practical decision-making, the behavior factors of decision makers often affect the final decision-making results. Regret theory is an important behavioral decision theory. Based on the regret theory, a novel decision-making method is proposed for the multiattribute decision-making problem with incomplete attribute weight information, and the attribute values are expressed by Atanassov intuitionistic fuzzy numbers. At first, a new distance of intuitionistic fuzzy sets is put forward based on the traditional Canberra distance. Then, we utilize it for the definition of the regret value (rejoice) for the attribute value of each alternative with the corresponding values of the positive point (negative point). The objective of this method is to maximize the comprehensive perceived utility of the alternative set by the decision maker. The optimal attribute weight vector is solved, and the optimal comprehensive perceived utility value of each alternative is obtained. Finally, according to the optimal comprehensive perceived utility value, the rank order of all alternatives is concluded.

Content Based Image Retrieval for Community Retrieval from Given Nationality using an Efficient Combination Algorithm

There is tremendous requirement of such technique which can fulfill the entire requirement for retrieval of an image from available dataset which comes under computer vision. In this paper we discussed about the one of the application of CBIR using an efficient combination of two techniques. The application is retrieval of people images from database that comes under minority. In this paper we used an efficient combination of color image histogram technique and edge orientation histogram technique by dividing original image into small subblocks. The feature vector is formed by combination of two features obtained by above methodologies. The final features obtained by query image will be compared with the feature vector of database images using a new Canberra Distance classifier. Proposed method is designed for multiple self-prepared and some collected from internet databases. Our method includes the efficient integration of features such as color, texture, shape and orientation. The proposed method is compared with state of art techniques to prove the stable and highest accuracy of proposed work.

Analysis of Braycurtis, Canberra and Euclidean Distance in KNN Algorithm

Classification is a technique used to build a classification model from a sample of training data. One of the most popular classification techniques is The K-Nearest Neighbor (KNN). The KNN algorithm has important parameter that affect the performance of the KNN Algorithm. The parameter is the value of the K and distance matrix. The distance between two points is determined by the calculation of the distance matrix before classification process by the KNN. The purpose of this study was to analyze and compare performance of the KNN using the distance function. The distance functions are Braycurtis Distance, Canberra Distance and Euclidean Distance based on an accuracy perspective. This study uses the Iris Dataset from the UCI Machine Learning Repository. The evaluation method used id 10-Fold Cross-Validation. The result showed that the Braycurtis distance method had better performance that Canberra Distance and Euclidean Distance methods at K=6, K=7, K=8 ad K=10 with accuracy values of 96 %.

A Proposed Framework for Emotion Recognition Using Canberra Distance Classifier

Facial Expression Recognition has become the preliminary research area due to its importance in human-computer interaction. Facial Expressions conveys the major part of information so it has vast applications in various fields. Many techniques have been developed in the literature but there is still a need to make the current expression recognition methods efficient. This paper represents proposed framework for face detection and recognizing six universal facial expressions such as happy, anger, disgust, fear, surprise, sad along with neutral face. Viola-Jones method and Face Landmark Detection method are used for face detection. Histogram of oriented gradients is used for feature extraction due to its superiority over other methods. To reduce the dimensionality of features Principal Component Analysis is used so that the maximum variation is preserved. Canberra distance classifier is used for classifying the expressions into different emotions. The proposed method is applied on Japanese Female Facial Expression Database and have evaluated that the proposed method outperforms many state-of-the-art techniques.