Color Invariant Object Recognition Using Entropic Graphs J. C. van Gemert, G. J. Burghouts, F. J. Seinstra, J. M. Geusebroek In International Journal of Imaging Systems and Technology 2006. [bibtex] [pdf] [url]

We present an object recognition approach using higher-order color invariant features with an entropy-based similarity measure. Entropic graphs offer an unparameterized alternative to common entropy estimation techniques, such as a histogram or assuming a probability distribution. An entropic graph estimates entropy from a spanning graph structure of sample data. We extract color invariant features from object images invariant to illumination changes in intensity, viewpoint, and shading. The Henze-Penrose similarity measure is used to estimate the similarity of two images. Our method is evaluated on the ALOI collection, a large collection of object images. This object image collection consists of 1000 objects recorded under various imaging circumstances. The proposed method is shown to be effective under a wide variety of imaging conditions.

@Article{vanGemertIJIST2006,
  author       = "van Gemert, J. C. and Burghouts, G. J. and Seinstra, F. J. and Geusebroek, J. M.",
  title        = "Color Invariant Object Recognition Using Entropic Graphs",
  journal      = "International Journal of Imaging Systems and Technology",
  number       = "5",
  volume       = "16",
  pages        = "146--153",
  year         = "2006",
  url          = "https://ivi.fnwi.uva.nl/isis/publications/2006/vanGemertIJIST2006",
  pdf          = "https://ivi.fnwi.uva.nl/isis/publications/2006/vanGemertIJIST2006/vanGemertIJIST2006.pdf",
  has_image    = 1
}