City Block Distance Measure Research Articles

New Interval Improved Fuzzy Partitions Fuzzy C-Means Clustering Algorithms under Different Distance Measures for Symbolic Interval Data Analysis

Symbolic interval data analysis (SIDA) has been successfully applied in a wide range of fields, including finance, engineering, and environmental science, making it a valuable tool for many researchers for the incorporation of uncertainty and imprecision in data, which are often present in real-world scenarios. This paper proposed the interval improved fuzzy partitions fuzzy C-means (IIFPFCM) clustering algorithm from the viewpoint of fast convergence that independently combined with Euclidean distance and city block distance. The two proposed methods both had a faster convergence speed than the traditional interval fuzzy c-means (IFCM) clustering method in SIDA. Moreover, there was a problem regarding large and small group division for symbolic interval data. The proposed methods also had better performance results than the traditional interval fuzzy c-means clustering method in this problem. In addition, the traditional IFCM clustering method will be affected by outliers. This paper also proposed the IIFPFCM algorithm to deal with outliers from the perspective of interval distance measurement. From experimental comparative analysis, the proposed IIFPFCM clustering algorithm with the city block distance measure was found to be suitable for dealing with SIDA with outliers. Finally, nine symbolic interval datasets were assessed in the experimental results. The statistical results of convergence and efficiency on performance revealed that the proposed algorithm has better results.

Performance Analysis of Distance Metric for Content Based Image Retrieval

Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.

Brachiopods classification based on fusion of contour and region based descriptors

In this paper, we propose a contour-region based shape descriptor for Brachiopods classification by using a combinations of Fourier descriptors and R-transform extracted from Radon transform. Fourier descriptors is supported by the well-developed and well-understood Fourier theory and are a powerful features for the recognition of two-dimensional connected shapes. We used the stable and complete Fourier descriptors proposed by Ghorbel to present the contour information. To depict shape interior content we used the R-transform. It is advantages lies in its low computational complexity and geometric invariance. We compared the proposed descriptor with curvature scale space, R-transform and Ghorbel descriptors using city block distance measure and our Brachiopods database. We present the experimental results to reveal the performance of the proposed descriptor which is independent on the starting points and efficient.

Quantitative Modeling of Ineffective Hematopoiesis in Myelodysplastic Syndrome Patients Yields Distinct Clinical Phenotypes and Can Identify a State of Disease Predictive of Loss of Response to Azacitidine

Background: The implementation of genomic data from myelodysplastic syndrome (MDS) patients into clinical practice requires its association with MDS disease activity. However, the determination of a genotype-phenotype correlation in MDS where the disease phenotype is ineffective hematopoiesis, is not straightforward. Part of the problem may be that disease activity reflected by a change in blast count, a change in a peripheral blood count, or the acquisition of a new cytogenetic abnormality does not currently provide any information about the likely dynamics of hematopoietic progenitors underlying this change. Being able to reliably infer a change in the early hematopoietic progenitor compartment of MDS patients where the disease clone likely resides that can account for the phenotype of ineffective hematopoiesis is an important step in the development of a tool to measure disease activity over time.We aim to apply a quantitative model of hematopoiesis based on parameters known to affect hematopoietic progenitor population dynamics, including rate of self-renewal, rate of proliferation, rate of differentiation, and rate of apoptosis. The mathematical formulation based on delay-differential equations has been successfully used to model the peripheral blood counts of CML and cyclic neutropenia. The goal is to identify unique MDS disease states based on the parameters described in the model, and to correlate these with both genotype and clinical outcomes.Methods: MDS patients with IPSS intermediate-2/high diagnosed during the period 2010-2017 at the London Health Science Centre had their bone marrow aspirate and biopsy data at diagnosis, laboratory data while on treatment with azacitidine and following disease progression, and transfusion history collected. The time-dependence of the peripheral blood counts for each patient were modeled using a delay-differential equation (Colijn and Mackey, 2005), with parameters representative of rates of proliferation, self-renewal, differentiation, and apoptosis of the stem cell and progenitor compartments. The model was integrated with MATLAB's dde23 routine and the model parameters were fit to the peripheral blood counts using gradient descent for the constrained non-linear least squares problem. The patients were separated into two clusters using the k-means clustering of all the model parameters using the city-block distance measure. The number of clusters were determined using the Calinski-Harabasz criterion.Results: Seventy-seven patients with a diagnosis of IPSS intermediate-2/high risk MDS were included in the analysis. Model fitting of the peripheral blood count data of 1000 simulated healthy patients whose blood count parameters were within their respective normal ranges over time yielded baseline variability of hematopoietic kinetic parameters (Figure 1). Two main groups of MDS patients could be identified from model fitting: MDS 1 and MDS 2, both of which could be best distinguished from the simulated healthy patients based on the parameter for the rate of megakaryocytic differentiation. MDS 1 and MDS 2 also had distinct parameters for the rates of hematopoietic progenitor proliferation, describing two different phenotypes of disease activity. The red blood cell count of a representative patient in MDS 1 is shown in Figure 2, where application of the Akaike information criterion identified the most likely time point when the hematopoietic kinetic parameters accounting for the peripheral red blood cell count changed after initiation of azacitidine. This time point preceded proven disease progression by 40 days.Conclusion: Parameterization of hematopoiesis with a model that can infer the kinetics of hematopoietic compartments in the bone marrow provides a powerful tool for measuring ineffective hematopoiesis in MDS patients. The resulting kinetic parameters can identify distinct groups of MDS patients based on the phenotype of their disease. The next step would be to correlate the different kinetic parameters distinguishing these groups to their disease genotypes using next-generation sequencing. On an individual patient level, these parameters can identify the most likely time when the disease phenotype changes. The development of a dynamic score predicting the change in disease activity based on hematopoietic kinetic parameters derived from peripheral blood count data over time is ongoing. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Code Converters with City Block Distance Measures for Classifying Epilepsy from EEG Signals

This paper aims to compare the performance of code-converter as a feature extraction technique followed by various distance measures such as City Block Distance (CBD) measure and Euclidean Distance (ED) measure for the perfect classification of epilepsy risk levels from Electroencephalography (EEG) Signals. From the extracted parameters such as sharp and spike waves, energy, peaks, duration, variance, events and covariance from the EEG Signals of an epileptic patient, the risk level of epilepsy is classified. The City Block Distance Measure and Euclidean Distance Measure is then applied to the Code Converter's risk level classification output in order to optimize the risk levels for the characterization of the patient. In this study, a group of 10 patients with known epilepsy findings are utilized here. The Performance metrics is computed with the help of parameters like Performance Index (PI) and Quality Values (QV).

Performance evaluation of local descriptors and distance measures on benchmarks and first-person-view videos for face identification

Face identification (FI) has made significant amount of progress in the last three decades. Its application is now moving towards wearable devices (like Google Glass and mobile devices) leading to the problem of FI on first-person-views (FPV) or ego-centric videos for scenarios like business networking, memory assistance, etc. In the existing literature, performance analysis of various image descriptors on FPV data is little known. In this paper, we evaluate six popular image descriptors: local binary patterns (LBP), scale invariant feature transform (SIFT), local phase quantization (LPQ), local intensity order pattern (LIOP), histogram of oriented gradients (HOG) and binarized statistical image features (BSIF) and ten different distance measures: Euclidean, Cosine, Chi square, Spearman, Cityblock, Minkowski, Correlation, Hamming, Jaccard and Chebychev with first nearest neighbor (1-NN) and support vector machines (SVM) as classifiers for FI task on both benchmark databases: FERET, AR, GT and FPV database collected using wearable devices like Google Glass (GG). Comparative analysis on these databases using various descriptors shows the superiority of BSIF with Cosine, Chi square and Cityblock distance measures using 1-NN as classifier over other descriptors and distance measures and even some of the current state-of-art benchmark database results.

Sports Video Classification Using Multi Scale Framework and Nearest Neighbor Classifier

Objectives: In order to achieve convenient sports video accessing without sequential scanning, automated sports video categorization is presented in this study. Methods/Analysis: In order to build efficient sports video categorizing system, edge features obtained from Non Subsampled Shearlet Transform (NSST) are taken into account. Then, sports genre categorization is done by Nearest Neighbor (NN) classifier due to its discriminative learning approach. The five sports category; tennis, cricket, volleyball, basketball and football are considered. Findings: To validate the proposed system based on NSST, experiments are carried using internal database video at frame level. Totally, 500 video clips are collected in which 100 video clips are gathered for each sports genre. The proposed system achieves maximum average classification accuracy of 94.80% at 4 directions of 2-scale NSST features while using city block distance measure in KNN classifier. For the same NSST features, the Euclidean, cosine and correlation distance measures gives an accuracy of 93.20%, 92.80% and 92% respectively. Conclusion/Application: The effectiveness of the system is clearly demonstrated by the experimental assessment. The proposed framework can adequately classify the sports video into one of the five predefined genre.

Comparative Analysis of Conventional, Real and Complex Wavelet Transforms for Face Recognition

recognition has been studied for many years in the context of biometrics and is one of the most successful applications of image analysis and understanding. Various methods, approaches and algorithms for recognition of human faces were proposed. In this paper, independent, comparative study of conventional discrete wavelet transform (DWT), real dual-tree discrete wavelet transform (R-DT-DWT), and complex dual-tree discrete wavelet transform (C-DT-DWT) based features for face recognition is carried out. In 2005, Delac et al. (26) presented an independent comparative study of PCA, ICA, and LDA on the FERET data set where it was concluded that no particular distance-metric combination is the best. In this paper we intend to bring further conclusions. Unlike the contribution by Delac et al., our conclusions are in context of DWT, R-DT-DWT, and C-DT-DWT. Moreover, these approaches are tested on nine different databases at different levels and under three different distance metrics, which allowed us to compare their performance independently. Our simulation results show that no particular distance-metric combination is the best across all standard benchmark face databases. However, the overall performance for city block distance measure was found to be better as compared to the Euclidean and cosine distance. Also, the performance for R-DT-DWT and C-DT-DWT based features were found equivalently efficient in many cases. So taking redundancy into consideration, it may be suggested to opt for R-DT-DWT for face efficient recognition.