Efficient Mining of Frequent and Distinctive Feature Configurations

T. Quack, V. Ferrari, B. Leibe, L. Van Gool

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

Abstract

We present a novel approach to automatically find spatial configurations of local features occurring frequently on instances of a given object class, and rarely on the background. The approach is based on computationally efficient data mining techniques and can find frequent configurations among tens of thousands of candidates within seconds. Based on the mined configurations we develop a method to select features which have high probability of lying on previously unseen instances of the object class. The technique is meant as an intermediate processing layer to filter the large amount of clutter features returned by low- level feature extraction, and hence to facilitate the tasks of higher-level processing stages such as object detection.
Original languageEnglish
Title of host publicationComputer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-8
Number of pages8
ISBN (Electronic)978-1-4244-1631-8
ISBN (Print)978-1-4244-1630-1
DOIs
Publication statusPublished - 1 Oct 2007

Keywords

  • data mining
  • feature extraction
  • object detection
  • computationally efficient data mining techniques
  • distinctive feature configurations
  • frequent feature configurations
  • Algorithm design and analysis
  • Computer vision
  • Data mining
  • Detectors
  • Feature extraction
  • Filters
  • Heart
  • Motorcycles
  • Object detection
  • Signal to noise ratio

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