Interest point detection using imbalance oriented selection

Qi Li, Jieping Ye, Chandra Kambhamettu

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application-facial identity representability evaluation-to show the value of imbalance oriented selection.

Original languageEnglish (US)
Pages (from-to)672-688
Number of pages17
JournalPattern Recognition
Issue number2
StatePublished - Feb 2008


  • Facial expression
  • Interest point detection
  • Repeatability

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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