A Novel Approach for Geometric Clustering based on Tensor Voting Framework

نویسندگان

  • Gabriele Lombardi
  • Alessandro Rozza
  • Elena Casiraghi
  • Paola Campadelli
چکیده

In this work we propose a novel geometric clustering algorithm based on the Tensor Voting Framework (TVF). More precisely, we propose the construction of a weighted graph by means of the information diffused by TVF during the vote casting step. This graph, which summarizes informations related to the manifold geometric structure, was used for clustering purposes. To this aim, we applied the well known Dijkstra and Ford Fulkerson algorithms to recursively separate weakly connected graph components. We performed preliminary tests, comparing our algorithm with that obtained by employing a weighted version of the ǫ-NN graph. The obtained results on both synthetic and real data show that the proposed technique is promising. To test our algorithm on real datasets, we preprocessed graylevel input images by extracting their edge pixel points.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Framework for Optimal Attribute Evaluation and Selection in Hesitant Fuzzy Environment Based on Enhanced Ordered Weighted Entropy Approach for Medical Dataset

Background: In this paper, a generic hesitant fuzzy set (HFS) model for clustering various ECG beats according to weights of attributes is proposed. A comprehensive review of the electrocardiogram signal classification and segmentation methodologies indicates that algorithms which are able to effectively handle the nonstationary and uncertainty of the signals should be used for ECG analysis. Ex...

متن کامل

Application of Combined Local Object Based Features and Cluster Fusion for the Behaviors Recognition and Detection of Abnormal Behaviors

In this paper, we propose a novel framework for behaviors recognition and detection of certain types of abnormal behaviors, capable of achieving high detection rates on a variety of real-life scenes. The new proposed approach here is a combination of the location based methods and the object based ones. First, a novel approach is formulated to use optical flow and binary motion video as the loc...

متن کامل

Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting

We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under our approach, manifolds in high-dimensional spaces are inferred by estimating geometric relationships among the input instances. Unlike conventional manifold learning, we do not perform dimensionality reduction, but i...

متن کامل

A Novel Ensemble Approach for Anomaly Detection in Wireless Sensor Networks Using Time-overlapped Sliding Windows

One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...

متن کامل

Region Segmentation based on Gaussian Dirichlet Process Mixture Model and its Application to 3D Geometric Stricture Detection

In general, image-based 3D scenes can now be found in many popular vision systems, computer games and virtual reality tours. So, It is important to segment ROI (region of interest) from input scenes as a preprocessing step for geometric stricture detection in 3D scene. In this paper, we propose a method for segmenting ROI based on tensor voting and Dirichlet process mixture model. In particular...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011