An improved feature vocabulary based method for image categorization

Frank Y. Shih, Alexander Sheppard

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The bags of feature and feature vocabulary based approaches have been presented for image categorization due to their simplicity and competitive performance. Some modified versions have been subsequently proposed, incorporating the methods such as adapted vocabularies, fast indexing, and Gaussian mixture models. In this paper, we propose an improvement of replacing the Harris-affine detection method by a random sampling procedure together with an increased number of sample points. Experimental results show that this new method improves categorization accuracy on a five-category problem using the Caltech-4 dataset. It is concluded that random sampling produces higher attainable point density and better categorization performance.

Original languageEnglish (US)
Pages (from-to)415-429
Number of pages15
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume25
Issue number3
DOIs
StatePublished - May 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Image categorization
  • feature detection
  • feature vocabulary
  • support vector machine

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