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TIP
2008
125views more  TIP 2008»
13 years 7 months ago
Segmentation by Fusion of Histogram-Based K-Means Clusters in Different Color Spaces
Abstract--This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simple...
Max Mignotte
KDD
2005
ACM
149views Data Mining» more  KDD 2005»
14 years 1 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 8 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
ICDCS
2009
IEEE
14 years 4 months ago
Q-Tree: A Multi-Attribute Based Range Query Solution for Tele-immersive Framework
Users and administrators of large distributed systems are frequently in need of monitoring and management of its various components, data items and resources. Though there exist s...
Md. Ahsan Arefin, Md. Yusuf Sarwar Uddin, Indranil...
DAGM
1998
Springer
13 years 12 months ago
Discrete Mixture Models for Unsupervised Image Segmentation
This paper introduces a novel statistical mixture model for probabilistic clustering of histogram data and, more generally, for the analysis of discrete co occurrence data. Adoptin...
Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann