HIERARCHICAL ONLINE IMAGE REPRESENTATION BASED ON 3D CAMERA GEOMETRY

Sang Min Yoon, Holger Graf

2009

Abstract

Within this paper, we present a hierarchical online image representation method with 3D camera position to efficiently summarize and classify the images on the web. The framework of our proposed hierarchical online image representation methodology is composed of multiple layers: at the lowest layer in the hierarchical structure, relationship between multiple images is represented by their recovered 3D camera parameters by automatic feature detection and matching. At the upper layers, images are classified using constrained agglomerative hierarchical image clustering techniques, in which the feature space established at the lowest layer consists of the camera’s 3D position. Constrained agglomerative hierarchical online image clustering method is efficient to balance the hierarchical layers whether images in the cluster are many or not. Our proposed hierarchical online image representation method can be used to classify online images within large image repositories by their camera view position and orientation. It provides a convenient way to image browsing, navigating and categorizing of the online images that have various view points, illumination, and partial occlusion.

References

  1. Bradley, P., Fayyad, U., and Reina, C., 1998. Scaling Clustering Algorithms to Large Databases. In Proceeding of ACM 4th DKK Conference.
  2. Brown, M., Lowe, D, G., 2005 Unsupervised 3D Object Recognition and Reconstruction in Unordered Datasets. 5th International Conference on 3D Imaging and Modelling.
  3. Cai, D., He, X., Li, Z., Ma, W. Y., and Wen, J. R., 2004 Hierarchical Clustering of WWW Image Search Results Using Visual Textual and Link Analysis. In Proceeding of 12th ACM Multimedia
  4. Chaman, L., and Sabharwal, 1993. Recovering 3D image parameters from corresponding two 2 images. In Proceeding of SIGGRAPP Symposium on Applied Computing.
  5. Cormark, R., 1971. A review of classification. Journal of the Royal Statistical Society Series A 134.
  6. Deng, Y., Manjunath, B. S., Kenney, C., Moore, M. S., and Shin, H., 2001 An efficient color representation for image retrieval. IEEE Transaction on Image Processing.
  7. Duda, R. D., Har, P. E., and Stork, D. G., 2001. Pattern Classification. Wiley second edition
  8. El Choubassi, M., Nefian, A. V., Kozintsev, I., Bouguet, J.-Y, and YiWu., 2007. Web Image Clustering. In Proceeding of IEEE ICASSP.
  9. Fergus, R., Fei-Fei, L., Pernona, P., Zisserman,A., 2005. Learning object categories from google's image search. In Proceeding of CVPR.
  10. Gao,B., Lie, T., Qin, T., Zheng, X., Cheng, Q., and Ma, W., 2005. Web image clustering by consistent utilization of visual features and surrounding texts. In the Proceeding of ACM Multimedia.
  11. Goldberger, J., Gordon, S., and Greenspan, H., 2006. Unsupervised Image-Set Clustering Using an Information Theoretic Framework. IEEE Transaction on Image Processing.
  12. Hartley, R., and Zisserman, A., 2004. Multiple View Geometry. Cambridge University Press.
  13. Jaffe, A., Naaman,M., Tassa, T., and Davis, M., 2006. Generating summaries and visualization for large collection of geo-referenced photographs. In the proceeding of ACM Workshop on Multimedia information Retrieval.
  14. Jhanwar, N., Chaudhuri, S., Seetharaman, G., Zavidovique, B., 2002. Content-based image retrieval using motif cooccurence matrix. In Proceeding of Image Vision Computing.
  15. Krishnamachari, S., and Abdel-Mottaleb, M., 1999. Hierarchical Clustering Algorithm for fast Image Retrieval.
  16. Naaman, M., Song, Y. J., Paepcke, A., and Garcia Molina, H., 2004. Automatic organization for digital photographs with geographical coordinates. In the Proceeding of ACM/IEEE Joint Conference on Digital Library .
  17. Lowe, D., 2004. Distinctive Image Features from ScaleInvariant Keypoints. IJCV.
  18. Rege, M., Dong, M., and Hua, J., 2007. Clustering web image with multi-modal features. In Proceeding of ACM Multimedia .
  19. Qian, R., van Beek, L. P., and Ibrahim Sezan, M., 2000. Image Retrieval Using Blob Histogram. In the Proceeding of ICME
  20. Snavely, N., Seitz, S. M., and Syeliski, R., 2006. Photo Tourism: Exploring collection in 3D. In the Proceeding of SIGGRAPH.
  21. Svoboda, T., Martinec, D., and Pajdla, T., 2005. A convenient multi-camera self-calibration for virtual environments. PRESENCE: Teleoperators and Virtual Environments.
  22. Wang, J., Sun,J., Quan, L., Tang, X., and Shum, H. Y., 2006. Picture Collage. In the Proceeding of CVPR.
  23. Xu, D., Wang, Y. and An, J., 2005. Applying a New Spatial Color Histogram in Mean Shift Based Tracking Algorithm. In Proceeding of Image and Vision Computing.
  24. Zeng, H. J., He, Q. C., Chen, Z., Ma, W. Y., and M, J. W., 2004. Learing to cluster web search results. In Proceeding of 27th International ACM SIGIR Conference.
  25. Zho, Y., and Karypis, G., 2005. Hierarchical Clustering Algorithms for Document Datasets. In Proceeding of Data Mining and Knowledge Discovery.
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Paper Citation


in Harvard Style

Min Yoon S. and Graf H. (2009). HIERARCHICAL ONLINE IMAGE REPRESENTATION BASED ON 3D CAMERA GEOMETRY . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 54-59. DOI: 10.5220/0001790600540059


in Bibtex Style

@conference{visapp09,
author={Sang Min Yoon and Holger Graf},
title={HIERARCHICAL ONLINE IMAGE REPRESENTATION BASED ON 3D CAMERA GEOMETRY},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={54-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001790600540059},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - HIERARCHICAL ONLINE IMAGE REPRESENTATION BASED ON 3D CAMERA GEOMETRY
SN - 978-989-8111-69-2
AU - Min Yoon S.
AU - Graf H.
PY - 2009
SP - 54
EP - 59
DO - 10.5220/0001790600540059