Retrieving Objects Using Local Integral Invariants

  • Alaa Halawani
  • Hashem Tamimi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)


The use of local features in computer vision has shown to be promising. Local features have several advantages including invariance to image transformations, independence of the background, and robustness in difficult situations like partial occlusions. In this paper we suggest using local integral invariants to extract local image descriptors around interest points and use them for the retrieval task. Integral invariants capture the local structure of the neighborhood around the points where they are computed. This makes them very well suited for constructing highly-discriminative local descriptors. We study two types of kernels used for extracting the feature vectors and compare the performance of both. The dimensionality of the feature vector to be used is investigated. We also compare our results with the SIFT features. Excellent results are obtained using a dataset that contains instances of objects that are viewed in difficult situations that include clutter and occlusion.


Feature Vector Recognition Rate Local Binary Pattern Interest Point Query Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alaa Halawani
    • 1
  • Hashem Tamimi
    • 2
  1. 1.Chair of Pattern Recognition and Image ProcessingUniversity of FreiburgFreiburgGermany
  2. 2.Computer Science Dept.University of TübingenTübingenGermany

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