Recognition using hybrid classifiers

Research output: Contribution to journalArticlepeer-review


A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a 'hybrid' classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply.

Original languageEnglish
Article number7182338
Pages (from-to)759-771
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number4
StatePublished - 1 Apr 2016

Bibliographical note

Publisher Copyright:
© 1979-2012 IEEE.


  • Large scale learning
  • Object detection
  • Object recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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