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.
Similar content being viewed by others
References
M. Beigi, A. Benitez, and S.-F. Chang, “MetaSEEk: A contentbased meta search engine for images,” in Storage and Retrieval for Image and Video Databases, SPIE Proceedings Series, San Jose, CA, 1998.
S. Brandt, “Use of shape features in content-based image retrieval,” Master's Thesis, Helsinki University of Technology, 1999. Available at http://www.cis.hut.fi/ picsom/publications.html.
S. Brandt, J. Laaksonen, and E. Oja, “Statistical shape features in content-based image retrieval,” in Proceedings of the 15th International Conference on Pattern Recognition (ICPR),Vol. 2, Barcelona, Spain, Sept. 2000, pp. 1066–1069.
A. Del Bimbo, Visual Information Retrieval, Morgan Kaufmann Publishers: San Mateo, CA, 1999.
M. Flickner, H. Sawhney, W. Niblack, et al. “Query by image and video content: The QBIC system,” IEEE Computer, Vol. 28, pp. 23–31, 1995.
N.J. Gunther and G. Beretta, “A benchmark for image retrieval using distributed systems over the internet: BIRDS-I,” Technical Report HPL-2000-162, HP Labs, 2000. Available at http://www.hpl.hp.com/techreports/2000/ HPL-2000-162.html. Statistical Shape Features for Content-Based Image Retrieval 197
T.S. Huang, S. Mehratra, and K. Ramchandran, “Multimedia analysis and retrieval system (MARS) project,” in Proceedings of the 33rd Annual Clinic on Library Application of Data Processing—Digital Image Access and Retrieval, University of Illinois at Urbana-Champaign, March 1996.
A.K. Jain and A. Vailaya, “Image retrieval using color and shape,” Pattern Recognition, Vol. 29, No. 8, pp. 1233–1244, 1996.
T. Kohonen, Self-Organizing Maps, 3rd edn. Vol. 30 of Springer Series in Information Sciences, Springer-Verlag: Berlin, 2001.
P. Koikkalainen, “Progress with the tree-structured selforganizing map,” in 11th European Conference on Artificial Intelligence, European Committee for Artificial Intelligence (ECCAI), A.G. Cohn (Ed.), John Wiley & Sons: New York, Aug. 1994, pp. 211–215.
P. Koikkalainen and E. Oja, “Self-organizing hierarchical feature maps,” in Proceedings of IJCNN-90, International Joint Conference on Neural Networks, San Diego, CA, 1990, pp. 279–284.
M. Koskela, “Content-based image retrieval with selforganizing maps,” Master's Thesis, Helsinki University of Technology, 1999. Available at http://www.cis.hut.fi/ picsom/publications.html.
J.T. Laaksonen, J.M. Koskela, S.P. Laakso, and E. Oja, “PicSOM—Content-based image retrieval with self-organizing maps,” Pattern Recognition Letters, Vol. 21, Nos. 13/14, pp. 1199–1207, 2000.
J. Laaksonen, M. Koskela, S. Laakso, and E. Oja, “Selforganizing maps as a relevance feedback technique in contentbased image retrieval,” Pattern Analysis & Applications, Vol. 4, Nos. 2/3, pp. 140–152, 2001.
M.S. Lew (Ed.), Principles of Visual Information Retrieval, Springer: Berlin, 2001.
W.Y. Ma and B.S. Manjunath, “NETRA: A toolbox for navigating large image databases,” in Proceedings of IEEE International Conference on Image Processing, Vol. I, Santa Barbara, California, Oct. 1997, pp. 925–928.
Overview of the MPEG-7 standard (version 5.0), March 2001. ISO/IEC JTC1/SC29/WG11 N4031.
A. Pentland, R.W. Picard, and S. Sclaroff, “Photobook: Contentbased manipulation of image databases,” International Journal of Computer Vision, Vol. 18, No. 3, pp. 233–254, 1996.
S. Ravela and R. Manmatha, “On computing global similarity in images,” in Proceedings of 4th IEEE Workshop on Applications of Computer Vision (WACV'98), Oct. 1998.
G. Salton and M.J. McGill, Introduction to Modern Information Retrieval, Computer Science Series, McGraw-Hill: New York, 1983.
B. Schiele, “Object recognition using multidimensional receptive field histograms,” Ph.D. Thesis, Institut Polytechnique de Grenoble, France, 1997. English translation.
E.L. Schwartz, “Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception,” Biological Cybernetics, Vol. 25, No. 4, pp. 181–194, 1977.
J.R. Smith and S.-F. Chang, “Searching for images and videos on the World Wide Web,” Technical Report 459-96-25, Columbia University CTR, Aug. 1996.
J.R. Smith and S.-F. Chang, “VisualSEEk: A fully automated content-based image query system,” in Proceedings of the ACM Multimedia'96, Nov. 1996.
M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision, International Thomson Computer Press, 1996.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Brandt, S., Laaksonen, J. & Oja, E. Statistical Shape Features for Content-Based Image Retrieval. Journal of Mathematical Imaging and Vision 17, 187–198 (2002). https://doi.org/10.1023/A:1020689721567
Issue Date:
DOI: https://doi.org/10.1023/A:1020689721567