In recent years, the effect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a efficiency and/or effectiveness perspective. Recent research results show that in high dimensional space, ...

In this paper, two new geodesic distance transforms for gray-scale images are presented. The first transform, the Distance Transform on Curved Space (DTOCS), performs the calculation with integer numbers. The second transform, the Weighted Distance Transform on Curved Space (WDTOCS), gives a weighted distance map with real numbers for an arbitrary gray-value image. Both transforms give a distan...

Hausdorr metrics are used in geometric settings for measuring the distance between sets of points. They have been used extensively in areas such as computer vision , pattern recognition and computational chemistry. While computing the distance between a single pair of sets under the Hausdorr metric has been well studied, no results were known for the Nearest Neighbor problem under Hausdorr metr...

Methods of nearest neighbors are essential in wide range of applications where it is necessary to estimate probability density (e.g. Bayes’s classifier, problems of searching in large databases). This paper contemplates on features of distribution of nearest neighbors’ distances in high-dimensional spaces. It shows that for uniform distribution of points in n-dimensional Euclidean space the dis...

BACKGROUND Implementation of REDD+ requires measurement and monitoring of carbon emissions from forest degradation in developing countries. Dry forests cover about 40 % of the total tropical forest area, are home to large populations, and hence often display high disturbance levels. They are susceptible to gradual but persistent degradation and monitoring needs to be low cost due to the low pot...

In recent years, the eeect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a eeciency and/or eeectiveness perspective. Recent research results show that in high dimensional space, the ...

We introduce the problem of repetitive nearest neighbor search in relevance feedback and propose an efficient search scheme for high dimensional feature spaces. Relevance feedback learning is a popular scheme used in content based image and video retrieval to support high-level concept queries. This paper addresses those scenarios in which a similarity or distance matrix is updated during each ...

BACKGROUND Although some 300 million Indian children travel to school every day, little is known about how they get there. This information is important for transport planners and public health authorities. This paper presents the development of a self-administered questionnaire and examines its reliability and validity in estimating distance and mode of travel to school in a low resource urban...

To classify time series by nearest neighbors, we need to specify or learn one or several distance measures. We consider variations of the Mahalanobis distance measures which rely on the inverse covariance matrix of the data. Unfortunately — for time series data — the covariance matrix has often low rank. To alleviate this problem we can either use a pseudoinverse, covariance shrinking or limit ...

one of the farmers' marketing decisions is how to sell their products to the market. they sell their products in different ways, for example pre-sell, local dealers and wholesalers,. this paper aims at investigating citrus distribution channels and evaluation of factors affecting the choice of each channels by mazandaran ‘s producers. the data was collected by simple random sampling method. 252...