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Clustering Using a Similarity Measure Based on Shared Nearest Neighbors
 IEEE Transactions on Computers
, 1973
"... AbstractA nonparametric clustering technique incorporating the concept of similarity based on the sharing of near neighbors is presented. In addition to being an essentially paraliel approach, the computational elegance of the method is such that the scheme is applicable to a wide class of practi ..."
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Cited by 172 (0 self)
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AbstractA nonparametric clustering technique incorporating the concept of similarity based on the sharing of near neighbors is presented. In addition to being an essentially paraliel approach, the computational elegance of the method is such that the scheme is applicable to a wide class of practical problems involving large sample size and high dimensionality. No attempt is made to show how a priori problem knowledge can be introduced into the procedure. Index TermsClustering, nonparametric, pattern recognition, shared near neighbors, similarity measure. I.
Dot pattern processing using voronoi neighborhoods
 IEEE Transactzons on Pattern Analyszs and Machzne Intellzgence
, 1982
"... AbstractA sound notion of the neighborhood of a point is essential for analyzing dot patterns. The past work in this direction has concentrated on identifying pairs of points that are neighbors. Examples of such methods include those based on a fixed radius, knearest neighbors, minimal spanning tr ..."
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Cited by 47 (5 self)
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AbstractA sound notion of the neighborhood of a point is essential for analyzing dot patterns. The past work in this direction has concentrated on identifying pairs of points that are neighbors. Examples of such methods include those based on a fixed radius, knearest neighbors, minimal spanning tree, relative neighborhood graph, and the Gabriel graph. This correspondence considers the use of the region enclosed by a point's Voronoi polygon as its neighborhood. It is argued that the Voronoi polygons possess intuitively appealing characteristics, as would be expected from the neighborhood of a point. Geometrical characteristics of the Voronoi neighborhood are used as features in dot pattern processing. Procedures for segmentation, matching, and perceptual border extraction using the Voronoi neighborhood are outlined. Extensions of the Voronoi definition to other domains are discussed. Index TermsClustering, computational complexity, dot patterns, Gabriel graph, knearest neighbors, matching, minimal spanning tree, neighborhood, neighbors, perceptual boundary extraction, relative neighborhood graph, Voronoi tessellation.
ScaleInvariant Image Recognition Based On Higher Order Autocorrelation Features
 Pattern Recognition
, 1996
"... We propose a framework and a complete implementation of a translation and scale invariant image recognition system for natural indoor scenes. The system employs higher order autocorrelation features of scale space data which permit linear classification. An optimal linear classification method is pr ..."
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Cited by 14 (1 self)
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We propose a framework and a complete implementation of a translation and scale invariant image recognition system for natural indoor scenes. The system employs higher order autocorrelation features of scale space data which permit linear classification. An optimal linear classification method is presented, which is able to cope with a large number of classes represented by many, as well as very few samples. In the course of the analysis of our system, we examine which numerical methods for feature transformation and classification show sufficient stability to fulfill these demands. The implementation has been extensively tested. We present the results of our own application and several classification benchmarks. Image recognition Face recognition Scale invariancy Scale space Higher order autocorrelation Optimal linear classification 1. INTRODUCTION The task of visual recognition which was defined by Marr (1) with the question: "What objects are where in the environment?" is still ...
Object Recognition by Alignment using Invariant Projections of Planar Surfaces
 IN PROC. 12TH ICPR
, 1994
"... In order to recognize an object in an image, we must determine the bestfit transformation which maps an object model into the image. In this paper, we first show that for features from coplanar surfaces which undergo linear transformations in space, there exists a class of transformations that yiel ..."
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Cited by 11 (3 self)
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In order to recognize an object in an image, we must determine the bestfit transformation which maps an object model into the image. In this paper, we first show that for features from coplanar surfaces which undergo linear transformations in space, there exists a class of transformations that yield projections invariant to the surface motions up to rotations in the image field. To use this property, we propose a new alignment approach to object recognition based on centroid alignment of corresponding feature groups built on these invariant projections of planar surfaces. This method uses only a single pair of 2D model and data pictures. Experimental results show that the proposed method can tolerate considerable errors in extracting features from images and can tolerate perturbations from coplanarity, as well as cases involving occlusions. As part of the method, we also present an operator for finding planar surfaces of an object using two model views and show its effectiveness by em...
An Examination Of Indexes For Determining The Number Of Clusters In Binary Data Sets
, 1999
"... An examination of 14 indexes for determining the number of clusters is conducted on articial binary data sets being generated according to various design factors. To provide a variety of clustering solutions the data sets are analyzed by dierent non hierarchical clustering methods. The purpose of th ..."
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Cited by 2 (1 self)
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An examination of 14 indexes for determining the number of clusters is conducted on articial binary data sets being generated according to various design factors. To provide a variety of clustering solutions the data sets are analyzed by dierent non hierarchical clustering methods. The purpose of the paper is to present the performance and the ability of an index to detect the proper number of clusters in a binary data set under various conditions and dierent diÆculty levels.
ISOTROPY CRITERIA AND ALGORITHMS FOR DATA CLUSTERING
, 2011
"... Given a set of points, the goal of data clustering is to group them into clusters, such that the internal homogeneity of points within each cluster contrasts to intercluster heterogeneity. Over the last fifty years, many methods for data clustering have been developed in diverse scientific communit ..."
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Given a set of points, the goal of data clustering is to group them into clusters, such that the internal homogeneity of points within each cluster contrasts to intercluster heterogeneity. Over the last fifty years, many methods for data clustering have been developed in diverse scientific communities. However, many of these methods suffer from several shortcomings, and are unable to handle the rich diversity of cluster structures that are usually present in data. We develop an unsupervised, nonparametric approach to data clustering that addresses these shortcomings. Our goal is to build on the strengths of these methods, while simultaneously offering innovative solutions to their limitations. In our cluster model, clusters are seen as groups of points, with overlapping neighborhoods, that have similar spatial structures that are in contrast with their surroundings. We use the isotropy of a point distribution to characterize spatial structure. We argue that identifying the isotropic density neighborhoods of a point, helps in the detection of a diversity of cluster structures that are challenging to many other methods. We develop three different criteria for identifying neighborhoods with isotropic density. The first criterion is based on examining properties of onedimensional projections in a hyperspherical neighborhood with uniform point distribution. The second and third criteria are based on the analysis of the force
Session No. 9 Pattern Recognition II Statistical Approaches A NONPARAMETRIC VALLEYSEEKING TECHNIQUE FOR CLUSTER ANALYSIS
"... The problem of clustering multivariate observations is viewed as the replacement of a set of vectors with a set of labels and representative vectors. A general criterion for clustering is derived as a measure of representation error. Some special cases are derived by simplifying the general criterio ..."
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The problem of clustering multivariate observations is viewed as the replacement of a set of vectors with a set of labels and representative vectors. A general criterion for clustering is derived as a measure of representation error. Some special cases are derived by simplifying the general criterion. A general algorithm for finding the optimum classification with respect to a given criterion is derived. For a particular case, the algorithm reduces to a repeated application of a straightforward decision rule which behaves as a valleyseeking technique. Asymptotic properties of the procedure are developed. Numerical examples are presented for the finite sample case. I.
ARTICLE NO. IV970623 Affine Matching of Planar Sets
"... To recognize an object in an image, we must determine the bestfit transformation which maps an object model into the image data. In this paper, we propose a new alignment approach to recovering those parameters, based on centroid alignment of corresponding feature groups built in the model and data ..."
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To recognize an object in an image, we must determine the bestfit transformation which maps an object model into the image data. In this paper, we propose a new alignment approach to recovering those parameters, based on centroid alignment of corresponding feature groups built in the model and data. To derive such groups of features, we exploit a clustering technique that minimizes intraclass scatter in coordinates that have been normalized up to rotations using invariant properties of planar patches. The present method uses only a single pair of 2D model and data pictures even though the object is 3D. Experimental results both through computer simulations and tests on natural pictures show that the proposed method can tolerate considerable perturbations of features including even partial occlusions of the surface. c ○ 1998 Academic Press 1.
Description
, 2012
"... Description This package provides most of the popular indices for cluster validation ready to use for the outputs produced by functions coming from the same package. It also proposes to user the best clustering scheme from the different results obtained by varying all combinations of number of clust ..."
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Description This package provides most of the popular indices for cluster validation ready to use for the outputs produced by functions coming from the same package. It also proposes to user the best clustering scheme from the different results obtained by varying all combinations of number of clusters,distance measures, and clustering methods.