Statistical Shape Features for Content-Based Image Retrieval

  • Authors:

  • Sami Brandt;Jorma Laaksonen;Erkki Oja

  • Affiliations:

  • Laboratory of Computational Engineering, Helsinki University of Technology, P.O. BOX 9203, FIN-02015 HUT, Finland. Sami.Brandt@hut.fi;Laboratory of Computer and Information Science, Helsinki University of Technology, P.O. BOX 5400, FIN-02015 HUT, Finland. Jorma.Laaksonen@hut.fi;Laboratory of Computer and Information Science, Helsinki University of Technology, P.O. BOX 5400, FIN-02015 HUT, Finland. Erkki.Oja@hut.fi

  • Venue:
  • Journal of Mathematical Imaging and Vision
  • Year:
  • 2002

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Abstract

In this article the use of statistical, low-level shape features in content-based image retrieval is studied. The emphasis is on such techniques which do not demand object segmentation. PicSOM, the image retrieval system used in the experiments, requires that features are represented by constant-sized feature vectors for which the Euclidean distance can be used as a similarity measure. The shape features suggested here are edge histograms and Fourier-transform-based features computed from the image after edge detection in Cartesian or polar coordinate planes. The results show that both local and global shape features are important clues of shapes in an image.