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In recent years, boosting has been successfully applied to many practical problems in pattern recognition and
 

Summary: Abstract
In recent years, boosting has been successfully applied
to many practical problems in pattern recognition and
computer vision fields such as object detection and
tracking. As boosting is an offline training process with
beforehand collected data, once learned, it cannot make
use of any newly arriving ones. However, an offline
boosted detector is to be exploited online and inevitably
there must be some special cases that are not covered by
those beforehand collected training data. As a result, the
inadaptable detector often performs badly in diverse and
changeful environments which are ordinary for many
real-life applications. To alleviate this problem, this paper
proposes an incremental learning algorithm to effectively
adjust a boosted strong classifier with domain-partitioning
weak hypotheses to online samples, which adopts a novel
approach to efficient estimation of training losses received
from offline samples. By this means, the offline learned
general-purpose detectors can be adapted to special online
situations at a low extra cost, and still retains good

  

Source: Ai, Haizhou - Department of Computer Science and Technology, Tsinghua University

 

Collections: Computer Technologies and Information Sciences