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To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2004 BoostMap: A Method for Efficient Approximate Similarity Rankings
 

Summary: To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2004
BoostMap: A Method for Efficient Approximate Similarity Rankings
Vassilis Athitsos, Jonathan Alon, Stan Sclaroff, and George Kollios
Computer Science Department
Boston University
111 Cummington Street
Boston, MA 02215
email: {athitsos, jalon, sclaroff, gkollios}@cs.bu.edu
Abstract
This paper introduces BoostMap, a method that can signif-
icantly reduce retrieval time in image and video database
systems that employ computationally expensive distance
measures, metric or non-metric. Database and query ob-
jects are embedded into a Euclidean space, in which sim-
ilarities can be rapidly measured using a weighted Man-
hattan distance. Embedding construction is formulated as
a machine learning task, where AdaBoost is used to com-
bine many simple, 1D embeddings into a multidimensional
embedding that preserves a significant amount of the prox-
imity structure in the original space. Performance is evalu-

  

Source: Athitsos, Vassilis - Department of Computer Science and Engineering, University of Texas at Arlington

 

Collections: Computer Technologies and Information Sciences