In a recent paper [13], the Fast Marching farthest point sampling strategy (FastFPS) for planar domains and curved manifolds was introduced. The version of FastFPS for curved manifolds discussed in the paper [13] deals with surface domains in triangulated form only. Due to a restriction of the underlying Fast Marching method, the algorithm further requires the splitting of any obtuse into acute...

A k nearest neighbor (kNN) classifier classifies a query instance to the most frequent class of its k nearest neighbors in the training instance space. For imbalanced class distribution, a query instance is often overwhelmed by majority class instances in its neighborhood and likely to be classified to the majority class. We propose to identify exemplar minority class training instances and gen...

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...

The realization that entanglement can affect macroscopic properties of bulk solid-state systems is a challenge in physics and Chemistry. Theoretical physicists often are considered the entanglement between nearest-neighbor (NN) spins and tried to find its characterizations in terms of macroscopic thermodynamics observables as magnetization and specific heat. Here, we focus on the entanglement b...

Conservative spatial queries, such as range search and nearest neighbor reclamation, involves only conditions on objects’ numerical properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbor query wo...

The pairwise nearest neighbor (PNN) method, also known as Ward's method belongs to the class of agglomerative clustering methods. The PNN method generates hierarchical clustering using a sequence of merge operations until the desired number of clusters is obtained. This method selects the cluster pair to be merged so that it increases the given objective function value least. The main drawback ...

This paper describes a technique that speeds up both the modelling and the sampling processes for a ramified object. By introducing the notion of substructure, we divide the ramified object into a set of ordered substructures, among which only a part of basic substructures is selected for implicit modelling and point sampling. Other substructures or even the whole object can then be directly in...

The k nearest neighbor classification method is one of the humblest method in conceptually and it is a top method in image mining. In this work, the enhanced k nearest neighbor (EKNN) technique has been implemented to identify the cancer and automatic classification of benign and malignant tissues in the huge amount of lung cancer image datasets. In this proposed system, we have used three stag...

Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful classification. The goal of this chapter is twofold. We first review the main ideas of Mahalanobis metric learning...