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Oneclass Boosting and Its Application to Classification Problems: (English)

Oneclass Boosting and Its Application to Classification Problems: (English)

          
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About the Book

We promote the concept of one-class learning and explore the role of background examples in learning processes. We propose to fit a stepwise additive model to minimize an adaptive quadratic loss. Experimental results on the NIST dataset show that our algorithm achieves comparable accuracy to AdaBoost, while it requires no individual training examples; robust to noise in the training data, and is computationally very efficient. Exponential loss is introduced to address the situation when there are small clusters in the object class. Randomization scheme is incorporated to handle high dimension of data. The resulted OneClassBoost converges much faster than AdaBoost, and achieves superior performance to AdaBoost in presence of small training sets. Behind the success of OneClassBoost, we believe that, simple statistics on background, rather than individual background examples, drive to shape the classification boundary. Also presented in this work is a framework for rapid objection detection. Promising results have been obtained using hierarchical search and attentional cascades. Hierarchical search is an efficient way of organizing objects in order to accommodate a large number of possible hypotheses. Cascade is a technique which allows background regions of an image to be quickly discarded. Our approach is to unify the two designs. The detection process corresponds to a sequential testing which is coarse-to-fine in both the exploration of object poses and the representation of background. The average detection time is 10%--15% of that of a single cascade. Both the one-class learning and the framework of test design is motivated by the task of face detection. Toward this end we have constructed a frontal face detection system which achieves an accuracy comparable to the state of the art on benchmark test sets, and capable of processing 7 frames per second.


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Product Details
  • ISBN-13: 9781243606273
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 246 mm
  • No of Pages: 182
  • Series Title: English
  • Weight: 336 gr
  • ISBN-10: 1243606274
  • Publisher Date: 01 Sep 2011
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 10 mm
  • Width: 189 mm

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