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» Mining from Large Image Sets
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KDD
2001
ACM
216views Data Mining» more  KDD 2001»
16 years 6 months ago
The distributed boosting algorithm
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Aleksandar Lazarevic, Zoran Obradovic
CVPR
2006
IEEE
16 years 8 months ago
A Simple Bayesian Framework for Content-Based Image Retrieval
We present a Bayesian framework for content-based image retrieval which models the distribution of color and texture features within sets of related images. Given a userspecified ...
Katherine A. Heller, Zoubin Ghahramani
GIS
2010
ACM
15 years 4 months ago
CBGIR: content-based geographic image retrieval
We describe and demonstrate CBGIR, a web-based system for performing content-based image retrieval in large sets of high-resolution overhead images. The system provides a familiar...
Shawn Newsam, Daniel Leung, Oscar Caballero, Justi...
ICDM
2006
IEEE
89views Data Mining» more  ICDM 2006»
16 years 11 days ago
On the Lower Bound of Local Optimums in K-Means Algorithm
The k-means algorithm is a popular clustering method used in many different fields of computer science, such as data mining, machine learning and information retrieval. However, ...
Zhenjie Zhang, Bing Tian Dai, Anthony K. H. Tung
ISBI
2007
IEEE
16 years 19 days ago
Harp Tracking Refinement Using Seeded Region Growing
Tagged magnetic resonance (MR) imaging makes it possible to image the motion of tissues such as the muscles found in the heart and tongue. The harmonic phase (HARP) method largely...
Xiaofeng Liu, Emi Murano, Maureen C. Stone, Jerry ...