Mean shift spectral clustering

نویسندگان

  • Umut Ozertem
  • Deniz Erdogmus
  • Robert Jenssen
چکیده

In recent years there has been a growing interest in clustering methods stemming from the spectral decomposition of the data affinity matrix, which are shown to present good results on a wide variety of situations. However, a complete theoretical understanding of these methods in terms of data distributions is not yet well understood. In this paper, we propose a spectral clustering based mode merging method for mean shift as a theoretically well-founded approach that enables a probabilistic interpretation of affinity based clustering through kernel density estimation. This connection also allows principled kernel optimization and enables the use of anisotropic variable-size kernels to match local data structures. We demonstrate the proposed algorithm’s performance on image segmentation applications and compare its clustering results with the well-known Mean Shift and Normalized Cut algorithms.

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 review of mean-shift algorithms for clustering

A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found using mean-shift algorithms. We describe the theory and practice behind clustering based on kernel density estimates and mean-shift algorithms. We discuss the ...

متن کامل

Spectral Clustering With

Clustering is a fundamental problem in machine learning with numerous important applications in statistical signal processing, pattern recognition, and computer vision, where unsupervised analysis of data classification structures are required. The current stateof-the-art in clustering is widely accepted to be the socalled spectral clustering. Spectral clustering, based on pairwise affinities o...

متن کامل

The Laplacian K-modes algorithm for clustering

In addition to finding meaningful clusters, centroid-based clustering algorithms such as K-means or mean-shift should ideally find centroids that are valid patterns in the input space, representative of data in their cluster. This is challenging with data having a nonconvex or manifold structure, as with images or text. We introduce a new algorithm, Laplacian K-modes, which naturally combines t...

متن کامل

Clustering with the Connectivity Kernel

Clustering aims at extracting hidden structure in dataset. While the problem of finding compact clusters has been widely studied in the literature, extracting arbitrarily formed elongated structures is considered a much harder problem. In this paper we present a novel clustering algorithm which tackles the problem by a two step procedure: first the data are transformed in such a way that elonga...

متن کامل

Evaluation of Performance of Fuzzy C Means and Mean Shift based Segmentation for Multi-Spectral Images

Image Segmentation has become very useful vision application because it can be used in many image processing applications. An image segmentation results in an images where each object is differentiated from other one. Many segmentation techniques have been proposed so far to get accurate segmentation results. This paper has focused on Mean Shift and Fuzzy C means clustering algorithm to segment...

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:
  • Pattern Recognition

دوره 41  شماره 

صفحات  -

تاریخ انتشار 2008