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Research at St Andrews

An efficient Naive Bayes approach to category-level object detection

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

Author(s)

Kasim Terzic, J. M.H. Du Buf

School/Research organisations

Abstract

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.

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Details

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1658-1662
Number of pages5
ISBN (Electronic)9781479957514
DOIs
Publication statusPublished - 28 Jan 2014

    Research areas

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

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ID: 255500434