Web-Based Dynamic Similarity Distance Tool

Similarity or distance measures is a well-known method and commonly used for calculating the distance between two samples of a dataset.Basically,the distance between the dataset samples is an important theory in multivariate analysis research.This paper proposes a tool that provides seven common distance methods that can be used in various research area.This tool is a web-based application which can be accessed through the internet browser.The objective of this tool is to introduce a web-based similarity distance application for many analysis and research purposes.Besides,a ranking method based on the Mean Average Precision is also implemented in this tool in order to increase the classification accuracies. This tool can process features that contain numerical values from any type of dataset

Fast search based on generalized similarity measure

Abstract This paper proposes a fast recognition method based on generalized similarity measure (GSM). The GSM achieves good recognition accuracy for face recognition, but has a scalability problem. Because the GSM method requires the similarity measures between a query and all samples to be calculated, the computational cost for recognition is in proportion to the number of samples. A reasonable approach to avoiding calculating all the similarity measures is to limit the number of samples used for calculation. Although approximate nearest neighbor search (ANNS) methods take this approach, they cannot be applied to the GSM-based method directly because they assume that similarity measure is the Euclidean distance. The proposed method embeds the GSM into the Euclidean distance so that it may be applied in existing ANNS methods. We conducted experiments on face, object, and character datasets, and the results show that the proposed method achieved fast recognition without dropping the accuracy