Simple Euclidean Distance Research Articles

Image Retrieval using Bit-plane Pixel Distribution

Users needing is to store and index the image data and retrieved the image on feature vector derived by the user. Content Based Image Retrieval (CBIR) is search engine to retrieving the desired image automatically from the large image database having different categories. Retrieving the relevant images from the database by using feature vector is the challenging and important task. It is also need to retrieve the images from variety of the domain that is the application of CBIR that domains are medicine, crime prevention ,Biometrics, architecture, Fashion and publishing. This paper present the method developed to search and retrieve the similar image using bit plane image. Bit plane images are formed by using threshold and using bit plane slicing. Mean, standard deviation and third moment of row and column pixel distribution of bit plane image is used as a feature vector. We use simple Euclidean distance to compute the similarity measures of images for Content Based Image Retrieval application. The average precision and average recall of each image category and over all precision and recall is considered for the performance measure.

Assessing the Residential Property Tax Revenue Impact of a Shopping Center

This study investigates the impact of a shopping center on surrounding housing prices. Instead of using simple Euclidean distance to measure proximity to the shopping center, this study creates multiple sizes of driving time buffers around the shopping center to better reflect real accessibility and to capture the distance-decay effects of the accessibility. We apply a spatial hedonic model with a series of driving time buffer variables to capture the influence of a shopping center in a metropolitan county in Tennessee. The findings suggest that households place a positive value on proximity to a shopping center with a travel time distance of 3–20 min in the case of the Turkey Creek Shopping Center, the largest and most recently developed shopping center in the Knoxville metropolitan area. Based on estimates of enhanced house values as they relate to proximity to the shopping center, we were able to calculate total annual property tax revenues ranging between $1.12 and $1.17 million for the county and city governments owing to the development of the Turkey Creek Shopping center.

Low Genetic Differentiation among Populations of the Great Plains Toad (Bufo cognatus) in Southern New Mexico

We examined the genetic population structure for the Great Plains Toad (Bufo cognatus) in the Chihuahuan Desert of southern New Mexico in order to discern at what spatial scale genetic differentiation is apparent. In addition, we tested whether habitats in the Chihuahuan Desert of southern New Mexico differed in their resistance to gene flow in B. cognatus. We used microsatellites to estimate genetic differentiation in populations that varied in distance from 1 to 60 km. Of 120 pairwise tests of genetic differentiation, 44 were significant. However, differentiation was low between all sites (FST = 0.0–0.087), almost all of the genetic variation being within populations (96.3%). Compared to published studies of other anuran species, populations of B. cognatus in southern New Mexico are among the most genetically homogenous anuran species. Significant isolation by distance did occur over all populations despite the genetic similarity, suggesting that differentiation does occur at a broader scale. In addition, several landscape-based models of gene flow were produced and tested against the allelic data. A community model assigned each plant community a different level of resistance to gene flow. This model was not found to describe the estimated genetic variation between populations better than simple Euclidean distance. However, the river model, which assigned low resistance to the aquatic habitats including the Rio Grande, described the estimated genetic variation better than Euclidean distance, suggesting that the Rio Grande, and potentially other rivers throughout the toad's range, may act as a route of dispersal for B. cognatus, reducing genetic differentiation among distant populations.

Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner

The goal of machine learning is to build automated systems that can classify and recognize complex patterns in data. The representation of the data plays an important role in determining what types of patterns can be automatically discovered. Many algorithms for machine learning assume that the data are represented as elements in a metric space. The performance of these algorithms can depend sensitively on the manner in which distances are measured. When data are represented as points in a multidimensional vector space, simple Euclidean distances are often used to measure the dissimilarity between different examples. However, such distances often do not yield reliable judgments; in addition, they cannot highlight the distinctive features that play a role in certain types of classification, but not others. Naturally, for different types of clustering, different ways of measuring dissimilarity were needed. In particular, different metrics for computing distances between feature vectors. This paper describes two algorithms for learning such distance metrics based on recent developments in convex optimization.

A logistic regression model for explaining urban development on the basis of accessibility: a case study of Naples

In this paper, the authors present a statistical modelling approach in order to explain the presence and development of built-up areas by means of a set of distinct accessibility indicators, so as to use these results in local planning studies, to test urban sustainability measures and to eventually forecast the impact of accessibility to transport systems on urban development. On the basis of encouraging results obtained in a previous study in the Netherlands, the authors apply a multinomial logistic regression, with urban development as the dependent variable and accessibility and context information as independent variables. The statistical model employs two kinds of accessibility measure: simple Euclidean distances, as well as a specific articulated accessibility indicator which takes into account spatial opportunities as well as distances. The results confirm the reliability and robustness of the chosen approach and confirm the limited usefulness of retrieving and deploying complex data to obtain composite indicators.

Intelligent sensor node based a low power ECG monitoring system

This paper presents a novel two-tier low power electrocardiogram (ECG) monitoring system which consists of a sensor nodes layer and a base station layer by employing a simple but effective Euclidean distance model. To save transmission power, a sensor node only transmits sensed data to a base station when the sensed data shows abnormality. For the low power implementation of the abnormality detection on a local power-hungry sensor node, we propose light-weight computation of Euclidean distance between a sensed ECG signal and a reference ECG signal. Experimental results show that the proposed scheme can reduce power consumption by 39-57% of the sensor nodes and accordingly prolongs the lifetime of the whole monitoring system.

Performance of recent advanced color-difference formulas using the standardized residual sum of squares index

The standardized residual sum of squares (STRESS) index was used to reevaluate four experimental datasets employed during the development of CIEDE2000, the current CIE recommended color-difference formula. This index enables statistical inferences not achievable by other metrics used commonly for performance evaluation. It was found that CIEDE2000 was statistically superior at a 95% confidence level to either CIE94, the previous recommended equation by the CIE, or the simple Euclidean distance in CIELAB, DeltaE*ab. Recent formulas based on the CIECAM02 color-appearance space and chroma-compressed variants of CIELAB were also evaluated and found to have only slightly reduced performance compared with CIEDE2000. These formulas have the advantage of simplicity and easier interpretation when used for quantifying color accuracy. Finally, each experimental dataset was evaluated separately rather than weight averaged as used during the development of CIEDE2000. Significant differences were found between datasets, suggesting that combining datasets may obscure important differences and that the practice of parameter optimization during formula development using combined data is likely suboptimal.

Landscape‐based geostatistics: a case study of the distribution of blue crab in Chesapeake Bay

AbstractGeostatistical techniques have gained widespread use in ecology and environmental science. Variograms are commonly used to describe and examine spatial autocorrelation, and kriging has become the method of choice for interpolating spatially‐autocorrelated variables. To date, most applications of geostatistics have defined the separation between sample points using simple Euclidean distance. In heterogeneous environments, however, certain landscape features may act as absolute or semi‐permeable barriers. This effective separation may be more accurately described by a measure of distance that accounts for the presence of barriers. Here we present an approach to geostatistics based on a lowest‐cost path (LCP) function, in which the cost of a path is a function of both the distance and the type of terrain crossed. The modified technique is applied to 13 years of survey data on blue crab abundance in Chesapeake Bay. Use of this landscape‐based distance metric significantly changed estimates of all three variogram parameters. In this case study, although local differences in kriging predictions were apparent, the use of the landscape‐based distance metric did not result in consistent improvements in kriging accuracy. Copyright © 2006 John Wiley & Sons, Ltd.

Modelling distances travelled to government health services in Kenya

To systematically evaluate descriptive measures of spatial access to medical treatment, as part of the millennium development goals to reduce the burden of HIV/AIDS, tuberculosis and malaria. We obtained high-resolution spatial and epidemiological data on health services, population, transport network, topography, land cover and paediatric fever treatment in four Kenyan districts to develop access and use models for government health services in Kenya. Community survey data were used to model use of government health services by febrile children. A model based on the transport network was then implemented and adjusted for actual use patterns. We compared the predictive accuracy of this refined model to that of Euclidean distance metrics. RESULTS Higher-order facilities were more attractive to patients (54%, 58% and 60% in three scenarios) than lower-order ones. The transport network model, adjusted for competition between facilities, was most accurate and selected as the best-fit model. It estimated that 63% of the population of the study districts were within the 1 h national access benchmark, against 82% estimated by the Euclidean model. Extrapolating the results from the best-fit model in study districts to the national level shows that approximately six million people are currently incorrectly estimated to have access to government health services within 1 h. Simple Euclidean distance assumptions, which underpin needs assessments and against which millennium development goals are evaluated, thus require reconsideration.

Mixed-genome microarrays reveal multiple serotype and lineage-specific differences among strains of Listeria monocytogenes.

Epidemiological studies and analysis of putative virulence genes have shown that Listeria monocytogenes has diverged into several phylogenetic divisions. We hypothesize that similar divergence has occurred for many genes that influence niche-specific fitness and virulence and that identifying these differences may offer new opportunities for the detection, treatment, and control of this important pathogen. To explore this issue further, we developed a microarray composed of fragmented DNA taken from 10 strains of L. monocytogenes. We then hybridized genomic DNA from 50 different strains to replicate arrays and analyzed the resulting hybridization patterns. A simple Euclidean distance metric permitted the reconstruction of previously described genetic relationships between serotypes, and only four microarray probes were needed to discriminate between the most important serotypes (1/2a, 1/2b, 1/2c, and 4). We calculated an index of linkage equilibrium from the microarray data and confirmed that L. monocytogenes has a strongly clonal population structure (I(A) = 3.85). Twenty-nine informative probes were retrieved from the library and sequenced. These included genes associated with repairing UV-damaged DNA, salt tolerance, biofilm formation, heavy metal transport, ferrous iron transport, and teichoic acid synthesis. Several membrane-bound lipoproteins and one internalin were identified, plus three phage sequences and six sequences with unknown function. Collectively, these data confirm that many genes have diverged between lineages of L. monocytogenes. Furthermore, these results demonstrate the value of mixed-genome microarrays as a tool for deriving biologically useful information and for identifying and screening genetic markers for clinically important microbes.

Metapopulation genetic structure and migration pathways in the land snail Helix aspersa: influence of landscape heterogeneity

The spatial genetic structuring of the land snail Helix aspersa was investigated for 32 colonies within an intensive agricultural area, the polders of the Bay of Mont-Saint-Michel (France). Given the habitat patchiness and environmental instability, the setting of H. aspersa colonies meets the broader view of a metapopulation structure. The identification of extrinsic barriers to migration and their impact on the genetic distribution was addressed through the genotyping of 580 individuals using a combined set of enzyme and microsatellite loci. To evaluate the distance as well as the direction over which the spatial genetic arrangement occurs, two-dimensional spatial autocorrelation analyses, Mantel tests of association and multivariate Mantel correlograms were used. Different connectivity networks and geographical distances based on landscape features were constructed to evaluate the effect of environmental heterogeneity and to test the adequacy of an isolation by distance model on the distribution of the genetic variability. Genetic divergence was assessed using either classical IAM-based statistics, or SMM-based genetic distances specifically designed to accommodate the mutational processes thought to fit microsatellite evolution (IAM: Infinite Allele Model; SMM: Stepwise Mutation Model). Genetic distances based only on genetic drift yielded the most plausible biologically meaningful interpretation of the observed spatial structure. Applying a landscape-based geographical distance which postulates that migration arises along roadside verges, hedges or irrigation canal embankments gave a better fit to an isolation by distance model than did a simple Euclidean distance. The progressive decline of genetic similarity with physical distance appeared to be environmentally induced, leading to functional migration pathways.

Total luminescence spectroscopy with pattern recognition for classification of edible oils.

Total luminescence spectroscopy combined with pattern recognition has been used to discriminate between four different types of edible oils, extra virgin olive (EVO), non-virgin olive (NVO), sunflower (SF) and rapeseed (RS) oils. Simplified fuzzy adaptive resonance theory mapping (SFAM), traditional back propagation (BP) and radial basis function (RBF) neural networks provided 100% classification for 120 samples, SFAM was found to be the most efficient. The investigation was extended to the adulteration of percentage v/v SF or RS in EVO at levels from 5% to 90% creating a total of 480 samples. SFAM was found to be more accurate than RBF and BP for classification of adulterant level. All misclassifications for SFAM occurred at the 5% v/v level resulting in a total of 99.375% correctly classified oil samples. The percentage of adulteration may be described by either RBF network (2.435% RMSE) or a simple Euclidean distance relationship of the principal component analysis (PCA) scores (2.977% RMSE) for v/v RS in EVO adulteration.

Classification of Upper Mississippi River Pools Based on Contiguous Aquatic/Geomorphic Habitats

ABSTRACT Navigation pools of the upper Mississippi River (UMR) vary greatly in terms of available contiguous aquatic/geomorphic habitats. These habitats are critical for the biotic diversity and overall productivity of the floodplain corridor of each pool. In this study, similarities among pools 4–26 and an open river reach (river kilometer 47–129) of the UMR were determined from multivariate analysis of eleven habitat types that were hydrologically-contiguous (non-leveed). Isolated floodplain habitats were not included in final analyses because this isolation limits their contribution to overall riverine productivity, in part due to a lack of hydrological connectivity to the main channel during the flood pulse. Cluster analysis based on simple Euclidean distance was used to produce two major pool groups and five pool subgroups. Important habitat variables in defining pool groups, as interpreted from principal components analysis (PCA) axis 1, were contiguous floodplain shallow aquatic area and contiguous impounded area. The habitat variable most important in defining pool subgroups, as interpreted from PCA axis 2, was tertiary channel. Most notably, pool 6 was more similar to pools 14–24 than other upper pools, and pools 19 and 25 were more similar to pools 4–13 than other lower pools. These results were quite different from those of two previous investigators, primarily because only areas of non-isolated aquatic habitat were considered.

RATES AND MECHANISMS OF SUBALPINE FOREST SUCCESSION ALONG AN ENVIRONMENTAL GRADIENT

This paper examines how rates and mechanisms of succession vary spatially and temporally in xeric, subalpine forests in Colorado, United States. We reconstructed 300 years of succession from limber pine (Pinus flexilis) to Engelmann spruce (Picea engelmanii) and subalpine fir (Abies lasiocarpa) in two watersheds recovering from the same fire. More than 1850 live and dead trees were cored and mapped in 25 plots systematically spaced along a topographic gradient. We used tree ring analysis to reconstruct dates of tree establishment and death. Relative species abundances and basal areas were charted at 20-yr intervals, and three measures of ecological similarity were also used to capture different elements of change: simple Euclidean distance, Euclidean distance in ordination space, and Horn’s CH similarity index. Successional rates for mesic lower slopes and north aspects were roughly twice those on south-facing side slopes. Rates were positively correlated with soil P, N, Fe, organic matter, and Parker’s t...

Floristic comparison of mesophytic semideciduous forests of the interior of the state of S�o Paulo, Southeast Brazil

The floristic composition (trees) of 26 forests in the state of Sao Paulo, Brazil, was compared using cluster analysis and Principal Coordinates Analysis (PCO) with simple Euclidean distance. The results obtained indicate the existence of two floristically distinct groups of forests. One group contains forests from higher areas (above 700 m) with a colder climate (Cfa and Cfb) and includes surveys from Angatuba, Atibaia, Guarulhos, Jundiai, Sao Jose dos Compos and Sao Paulo. The second group is floristically less homogeneous and includes forests of the central and western parts of the state, usually at lower altitudes (500–700 m) and subject to hotter climatic conditions (Cwa).

Influence of polarization functions on atomic properties of bridge N and H atoms of HCN … HCN

The effect of different types of core and valence polarization functions on the equilibrium geometry, electric fields, Cioslowsky and Mulliken atomic charges, dissociation energy and CH stretching frequency changes for HCN … HCN was investigated. Basis sets for C, N and H were constructed (6-GCM) from the 6-31G basis sets to improve the Hellmann-Feynman forces and to describe adequately the asymptotic behavior of the wavefunction in the valence region. This new procedure provided accurate basis sets. Calculations of equilibrium geometry, total energies, harmonic frequencies and electric fields of some test molecules showed excellent agreement with accurate results. The resulting basis sets were considerably large in size and the need for reliable calculations of the hydrogen bonds of the HCN dimer with a 6-31G basis set using different procedures to include polarization functions was investigated. The properties calculated at the Hartree-Fock level of theory (SCF) for the HCN dimer using the 6-GCM basis sets were taken as reference. Three sets of polarization functions derived from the original 6-31G basis set were used with the original 6-31G basis set at the SCF level of theory for the HCN dimer. The agreement between the properties calculated with the polarized 6-31G basis set and the standard 6-GCM basis was analyzed using the simple statistical Euclidean distance. Comparison of the Euclidean distances for molecular properties (electric field, Cioslowsky charges, bond lengths, change in CH stretching frequency and dissociation energies) suggests that 6-31G polarized with the Pople's original d type functions or mixing Pople's polarization function with polarization that improves the Hellmann-Feynman forces are in closer agreement with the calculations using the 6-GCM basis set. If molecular properties more directly related to the hydrogen bond are improved, other molecular properties are also better characterized. Euclidean distances for Mulliken charges have shown a different tendency. If only Mulliken charges are considered, the polarization of hydrogen is sufficient.

Efficient and effective Querying by Image Content

In the QBIC (Query By Image Content) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, shape, position, and dominant edges of image objects and regions. Potential applications include medical (“Give me other images that contain a tumor with a texture like this one”), photo-journalism (“Give me images that have blue at the top and red at the bottom”), and many others in art, fashion, cataloging, retailing, and industry. We describe a set of novel features and similarity measures allowing query by image content, together with the QBIC system we implemented. We demonstrate the effectiveness of our system with normalized precision and recall experiments on test databases containing over 1000 images and 1000 objects populated from commercially available photo clip art images, and of images of airplane silhouettes. We also present new methods for efficient processing of QBIC queries that consist of filtering and indexing steps. We specifically address two problems: (a) non Euclidean distance measures; and (b) the high dimensionality of feature vectors. For the first problem, we introduce a new theorem that makes efficient filtering possible by bounding the non-Euclidean, full cross-term quadratic distance expression with a simple Euclidean distance. For the second, we illustrate how orthogonal transforms, such as Karhunen Loeve, can help reduce the dimensionality of the search space. Our methods are general and allow some “false hits” but no false dismissals. The resulting QBIC system offers effective retrieval using image content, and for large image databases significant speedup over straightforward indexing alternatives. The system is implemented in X/Motif and C running on an RS/6000.

A neural clustering approach for high resolution radar target classification

Neural learning techniques, the Self-Organizing Feature Map and Learning Vector Quantization, have been applied to the automatic target recognition (ATR) problem in the presence of high range resolution radar target signatures. The database is collected by placing the targets on a rotary turntable and slowly turning them over a complete 360° azimuth while the radar signatures are collected. Our pattern recognition system is composed of a feature identifier and a classifier. A simple Euclidean distance classifier using those identified features provides a baseline of 97% mean probability of correct classification.

Cryptic Species of Hylid Marsupial Frogs in Peru

Cryptic Species of Hylid Marsupial Frogs in Peru

A weighted cepstral distance measure for speech recognition

A weighted cepstral distance measure is proposed and is tested in a speaker-independent isolated word recognition system using standard DTW (dynamic time warping) techniques. The measure is a statistically weighted distance measure with weights equal to the inverse variance of the cepstral coefficients. The experimental results show that the weighted cepstral distance measure works substantially better than both the Euclidean cepstral distance and the log likelihood ratio distance measures across two different databases. The recognition error rate obtained using the weighted cepstral distance measure was about 1 percent for digit recognition. This result was less than one-fourth of that obtained using the simple Euclidean cepstral distance measure and about one-third of the results using the log likelihood ratio distance measure. The most significant performance characteristic of the weighted cepstral distance was that it tended to equalize the performance of the recognizer across different talkers.