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ICDM
2005
IEEE
189views Data Mining» more  ICDM 2005»
15 years 11 months ago
Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering
We present a novel approach for clustering sequences of multi-dimensional trajectory data obtained from a sensor network. The sensory time-series data present new challenges to da...
Jie Yin, Qiang Yang
ICDAR
2003
IEEE
15 years 11 months ago
A Low-Cost Parallel K-Means VQ Algorithm Using Cluster Computing
In this paper we propose a parallel approach for the Kmeans Vector Quantization (VQ) algorithm used in a twostage Hidden Markov Model (HMM)-based system for recognizing handwritte...
Alceu de Souza Britto Jr., Paulo Sergio Lopes de S...
ICCV
2009
IEEE
1556views Computer Vision» more  ICCV 2009»
16 years 11 months ago
Kernel Methods for Weakly Supervised Mean Shift Clustering
Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of th...
Oncel Tuzel, Fatih Porikli, Peter Meer
ICMCS
2006
IEEE
94views Multimedia» more  ICMCS 2006»
16 years 6 days ago
Semantic Labeling of Multimedia Content Clusters
In this paper we present a novel approach for labeling clusters of multimedia content that leverages supervised classification techniques in conjunction with unsupervised cluster...
Jelena Tesic, John R. Smith
OTM
2005
Springer
15 years 11 months ago
Web Image Semantic Clustering
This paper provides a novel Web image clustering methodology based on their associated texts. In our approach, the semantics of Web images are firstly represented into vectors of t...
Zhiguo Gong, Leong Hou U, Chan Wa Cheang