A distance transform (DT) converts a binary image consisting of foreground (feature) and background (non-feature) pixels into a gray level image where each pixel contains the distance from the corresponding pixel to the nearest foreground pixel. The computation of the exact Euclidean DT is computationally complex task and, therefore, approximations are typically utilized. In this paper, an area...

This work introduces Mutual Proximity, an unsupervised method which transforms arbitrary distances to similarities computed from the shared neighborhood of two data points. This reinterpretation aims to correct inconsistencies in the original distance space, like the hub phenomenon. Hubs are objects which appear unwontedly often as nearest neighbors in predominantly high-dimensional spaces. We ...

in this paper, we ask “can proximity to towns reduces poverty in rural areas?” for this study we have mapped all the villages in orissa. the study is based on small area poverty estimation methodology as well as the secondary data covering 47,395 villages and 109 towns. our main findings are (i) there is a relationship between the distance to nearest town and rural poverty and (ii) poverty leve...

This paper contains three contributions to the problem of learning a Mahalanobis distance. First, a general framework for kernelizing Mahalanobis distance learners is presented. The framework allows existing algorithms to learn a Mahalanobis distance in a feature space associated with a pre-specified kernel function. The framework is then used for kernelizing three well-known learners, namely, ...

The k-local hyperplane distance nearest neighbors classification (HKNN) builds a non-linear decision surface with maximal local margin in the input space, with invariance inferred from the local neighborhood rather than the prior knowledge, so that it performs very well in many applications. However, it still cannot be comparable with human being in classification on the noisy, the sparse, and ...

A novel solution is presented to the Nearest Neighbor Problem that is specifically tailored for determining correspondences within the Iterative Closest Point Algorithm. The reference point set P is preprocessed by calculating for each point ~pi 2 P that neighborhood of points which lie within a certain distance of ~pi. The points within each -neighborhood are sorted by increasing distance to t...

In this paper we present a new framework for emotion recognition from speech based on a similarity concept called Weighted Ordered Classes-Nearest Neighbors (WOC-NN). Unlike the k-nearest neighbor, an instance-similarity based method; WOC-NN computes similarities between a test instance and a class pattern of each emotion class. An emotion class pattern is a representation of its ranked neighbo...

A. Introduction Recommendation systems are an active topic in research and industry. The technique of collaborative filtering is especially successful in generating personalized recommendations. More than a decade of research has resulted in numerous algorithms, out of which, we have chosen K-Nearest Neighborhood (K-NN) model to predict the ratings for the songs. This model is an item-based alg...

Mobile query processing is, actually, a very active research field. Range and nearest neighbor queries are common types of queries in spatial databases and location based services (LBS). In this paper, we focus on finding nearest neighbors of a query point within a certain distance range. An example of query, frequently met in LBS, is “Find all the nearest gas stations within 2 miles neighborho...