This paper presents a highly efficient algorithm for detecting and tracking humans and objects in video surveillance sequences. Mean shift clustering is applied on backgrounddifferenced image sequences. For efficiency, all calculations are performed on integral images. Novel corresponding exponential integral kernels are introduced to allow the application of nonuniform kernels for clustering, ...

Multilayer graphs encode different kind of in-teractions between the same set of entities.When one wants to cluster such a multilayergraph, the natural question arises how oneshould merge the information from differentlayers. We introduce in this paper a one-parameter family of matrix power means formerging the Laplacians from different layersand analyze it in ex...

Although consistency is a minimum requirement of any estimator, little is known about consistency of the mean partition approach in consensus clustering. This contribution studies the asymptotic behavior of mean partitions. We show that under normal assumptions, the mean partition approach is consistent and asymptotic normal. To derive both results, we represent partitions as points of some geo...

In this paper, the proposed approach is an unique combination of two most popular clustering algorithms Particle Swarm Optimization (PSO) and K-Means to achieve better clustering result. Clustering is a technique of grouping homogeneous objects of a dataset with aim to extract some meaningful pattern or information. K-Means algorithm is the most popular clustering algorithm because of its easy ...

In this paper, a novel approach to face clustering is proposed. The aim is the extraction of planes of a mesh acquired from a 3D reconstruction process. In this context, as 3D coordinates points are inevitably affected by error, robustness is the main focus. The method is based on mean shift clustering paradigm, devoted to separate the modes of a multimodal density by using a kernel-based techn...

Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are constrained to be spherically symmetric and their number has to be known a priori. In nonparametric clustering methods, like the one based on mean shift, these limitations are eliminated but the amount of computation bec...

Cluster analysis or clustering is a technique storing logically similar objects together physically. This physical storage is referred as classes in clustering. The data available as input for clustering can be of various types e.g. image, text etc. This process is carried out by different algorithms such as k-mean, fuzzy-C etc. In this paper light is thrown out on various aspects related to cl...

A general scheme for divisive hierarchical clustering algorithms is proposed. It is made of three main steps : first a splitting procedure for the subdivision of clusters into two subclusters, second a local evaluation of the bipartitions resulting from the tentative splits and, third, a formula for determining the nodes levels of the resulting dendrogram. A handfull of such algorithms is given...

In this paper, two short-time spectral amplitude estimators of the speech signal are derived based on a parametric formulation of the original generalized spectral subtraction method. The objective is to improve the noise suppression performance of the original method while maintaining its computational simplicity. The proposed parametric formulation describes the original method and several of...

 In this paper a variant of the classical hierarchical cluster analysis is reported. This agglomerative (bottom-up) cluster technique is referred to as the Adaptive Mean-Linkage Algorithm. It can be interpreted as a linkage algorithm where the value of the threshold is conveniently up-dated at each interaction. The superiority of the adaptive clustering with respect to the average-linkage algo...