An efficient Naive Bayes approach to category-level object detection

Kasim Terzic, J. M.H. Du Buf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


We present a fast Bayesian algorithm for category-level object detection in natural images. We modify the popular Naive Bayes Nearest Neighbour classification algorithm to make it suitable for evaluating multiple sub-regions in an image, and offer a fast, filtering-based alternative to the multi-scale sliding window approach. Our algorithm is example-based and requires no learning. Tests on standard datasets and robotic scenarios show competitive detection rates and real-time performance of our algorithm.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781479957514
Publication statusPublished - 28 Jan 2014


  • Computer vision
  • Nearest neighbour
  • Object detection
  • Real time systems
  • Robot vision


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