Statistical Shape Features in Content-Based Image Retrieval

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Referenz

Jorma Laaksonen, Erkki Oja and Sami Brandt: Statistical Shape Features in Content-Based Image Retrieval. In Proceedings of the 15th International Conference on Pattern Recognition, Vol.2, pp. 1062 - 1065, Sep 2000

DOI

http://dx.doi.org/10.1109/ICPR.2000.906258

Abstract

In this article the use of shape features in content-based image retrieval is studied. The emphasis is on 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 for an edge image in Cartesian and polar coordinate planes. The results show that both local and global shape features are important clues of shapes in an image

Extended Abstract

Bibtex

@INPROCEEDINGS{906258,
author={S. Brandt and J. Laaksonen and E. Oja},
booktitle={Pattern Recognition, 2000. Proceedings. 15th International Conference on},
title={Statistical shape features in content-based image retrieval},
year={2000},
volume={2},
pages={1062-1065 vol.2},
keywords={Fourier transforms;edge detection;feature extraction;image retrieval;search engines;statistical analysis;visual databases;Euclidean distance;Fourier transform;PicSOM;content-based image retrieval;edge detection;edge histograms;feature vectors;image database;search engine;statistical shape features;Content based retrieval;Feature extraction;Histograms;Image databases;Image retrieval;Image segmentation;Laboratories;Search engines;Shape;Spatial databases},
doi={10.1109/ICPR.2000.906258},
url={http://dx.doi.org/10.1109/ICPR.2000.906258 http://de.evo-art.org/index.php?title=Statistical_Shape_Features_in_Content-Based_Image_Retrieval },
ISSN={1051-4651},
month={},
}

Used References

M. Beigi , A. Benitez and S.-F. Chang, "MetaSEEk: A content-based meta search engine for images", Storage and Retrieval for Image and Video Databases, 1998

S. Brandt, Use of shape features in content-based image retrieval, 1999

M. Flickner , H. Sawhney and W. Niblack, "Query by image and video content: The QBIC system", IEEE Computer, vol. 28, pp. 23-31, 1995 http://dx.doi.org/10.1109/2.410146

T. S. Huang , S. Mehratra and K. Ramchandran, "Multimedia analysis and retrieval system (MARS) project", Proceedings of the 33rd Annual Clinic on Library Application of Data Processing - Digital Image Access and Retrieval, 1996

A. K. Jain and A. Vailaya, "Image retrieval using color and shape", Pattern Recognition, vol. 29, no. 8, pp. 1233-1244, 1996 http://dx.doi.org/10.1016/0031-3203(95)00160-3

T. Kohoncn, Self-Organizing Maps, vol. 30, 1997, Springer-Verlag

M. Koskela, Content-based image retrieval with self-organizing maps, 1999

J. Laaksonen , M. Koskela and E. Oja, "Content-based image retrieval using self-organizing maps", Proceedings of the Third International Conference on Visual Information and Information Systems, VISUAL\'99, pp. 541-548, 1999 http://dx.doi.org/10.1007/3-540-48762-X_67

W. Y. Ma and B. S. Manjunath, "NETRA: A toolbox for navigating large image databases", Proceedings of IEEE International Conference on Image Processing, vol. I, pp. 925-928, 1997 http://dx.doi.org/10.1109/ICIP.1997.647976

A. Pentland , R. W. Picard and S. Sclaroff, "Photobook: Content-based manipulation of image databases", International Journal of Computer Vision, vol. 18, no. 3, pp. 233-254, 1996 http://dx.doi.org/10.1007/BF00123143

J. R. Smith and S.-F. Chang, "VisualSEEk: a fully automated content-based image query system", Proceedings of the ACM Multimedia \'96, 1996 http://dx.doi.org/10.1145/244130.244151

Links

Full Text

internal file


Sonstige Links