Online Indexing Structure for Big Image Data used for 3D Reconstruction

Konstantinos Makantasis, Yannis Katsaros, Anastasios Doulamis, Matthaios Bimpas

Abstract

One of the main characteristics of Internet era is the free and online availability of extremely large collections of images. Although the proliferation of millions of shared photos provide a unique opportunity for cultural heritage e-documentation, the main difficulty is that Internet image datasets are unstructured. For this reason, this paper aims to describe a new image indexing scheme with application in 3D reconstruction. The presented approach is capable, on the one hand to index images in a fast and accurate way and on the other to select form an image dataset the most appropriate images for 3D reconstruction, improving this way reconstruction computational time, while simultaneously keeping the same reconstruction performance.

References

  1. Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S. M., and Szeliski, R. (2011). Building rome in a day. Commun. ACM, 54(10):105112.
  2. Arampatzis, A., Zagoris, K., and Chatzichristofis, S. A. (2011). Dynamic two-stage image retrieval from large multimodal databases. In Advances in Information Retrieval, Lecture Notes in Computer Science, pages 326-337. Springer Berlin Heidelberg.
  3. Bay, H., Tuytelaars, T., and Gool, L. V. (2006). SURF: speeded up robust features. In Computer Vision ECCV 2006, number 3951 in Lecture Notes in Computer Science, pages 404-417. Springer Berlin Heidelberg.
  4. Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). BRIEF: binary robust independent elementary features. In Computer Vision ECCV 2010, number 6314 in Lecture Notes in Computer Science, pages 778- 792. Springer Berlin Heidelberg.
  5. Cayton, L. (2006). Algorithms for manifold learning. Technical Report CS2008-0923, University of California, San Diego, Tech.
  6. Chum, O., Philbin, J., Sivic, J., Isard, M., and Zisserman, A. (2007). Total recall: Automatic query expansion with a generative feature model for object retrieval. In IEEE 11th Intern. Conf. on Comp. Vision. ICCV, pages 1-8.
  7. Cox, M. A. A. and Cox, T. F. (2008). Multidimensional scaling. In Handbook of Data Vis., Springer Handbooks Comp.Statistics, pages 315-347. Springer.
  8. Doulamis, N., Yiakoumettis, C., and Miaoulis, G. (2012). On-line spectral learning in exploring 3d large scale geo-referred scenes. In Progress in Cultural Heritage Preservation, pages 109-118. Springer.
  9. Hinton, G. E. and Roweis, S. T. (2002). Stochastic neighbor embedding. In Becker, S., Thrun, S., and Obermayer, K., editors, Advances in NIPS 15, pages 833-840.
  10. Janssens, J., Huszar, F., Postma, E., and van den Herik, J. (2012). Stochastic outlier selection. Technical Report TiCC TR 2012-001, Tilburg University, Netherlands.
  11. Kalantidis, Y., Tolias, G., Avrithis, Y., Phinikettos, M., Spyrou, E., Mylonas, P., and Kollias, S. (2011). VIRaL: visual image retrieval and localization. Multimedia Tools and Applications, 51(2):555-592.
  12. Kekre, D. H. B., Sarode, T. K., Thepade, S. D., and Vaishali, V. (2011). Improved texture feature based image retrieval using kekres fast codebook generation algorithm. In Thinkquest, pages 143-149. Springer India.
  13. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  14. Lv, Q., Josephson, W., Wang, Z., Charikar, M., and Li, K. (2007). Multi-probe LSH: efficient indexing for highdimensional similarity search. In P33rd Inter. Conf on VLDB, VLDB 7807, page 950961, Vienna, Austria.
  15. Murthy, V. S. V. S., Kumar, S., and Rao, P. S. (2010). Content based image retrieval using hierarchical and kmeans clustering techniques. Intern. Journal of Engineering Science and Technology, 2(3).
  16. Papadopoulos, S., Zigkolis, C., Kompatsiaris, Y., and Vakali, A. (2010). Cluster-based landmark and event detection on tagged photo collections. IEEE Multimedia.
  17. Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A. (2007). Object retrieval with large vocabularies and fast spatial matching. In IEEE Conf. on Comp. Vision and Pattern Recognition. CVPR, pages 1-8.
  18. Rosin, P. (1999). Measuring corner properties. Computer Vision and Image Understanding, pages 291-307.
  19. Rosten, E. and Drummond, T. (2006). Machine learning for high-speed corner detection. In Computer Vision ECCV 2006, number 3951 in Lecture Notes in Computer Science, pages 430-443. Springer Berlin Heidelberg.
  20. Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). ORB: an efficient alternative to SIFT or SURF. In 2011 IEEE Intern. Conf. on Comp. Vision (ICCV), pages 2564-2571.
  21. Simon, I., Snavely, N., and Seitz, S. M. (2007). Scene summarization for online image collections. In IEEE 11th Intern. Conf. on Comp. Vision. ICCV, pages 1-8.
  22. Winter, M. E. (1999). N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. volume 3753, pages 266-275.
  23. Wu, C., Agarwal, S., Curless, B., and Seitz, S. (2011). Multicore bundle adjustment. In IEEE Conf. on Comp. Vision and Pattern Recognition (CVPR), pages 3057- 3064.
  24. Wu, C., Agarwal, S., Curless, B., and Seitz, S. (2012). Schematic surface reconstruction. In IEEE Conf. on Comp. Vision and Pattern Recognition (CVPR), pages 1498-1505.
  25. Yiakoumettis, C., Doulamis, N., Miaoulis, G., and Ghazanfarpour, D. (2014). Active learning of users preferences estimation towards a personalized 3d navigation of geo-referenced scenes. GeoInformatica, 18(1):27- 62.
  26. Zheng, Y.-T., Zhao, M., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T.-S., and Neven, H. (2009). Tour the world: Building a web-scale landmark recognition engine. In IEEE Conf. on Comp. Vision and Pattern Recognition. CVPR, pages 1085- 1092.
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Paper Citation


in Harvard Style

Makantasis K., Katsaros Y., Doulamis A. and Bimpas M. (2016). Online Indexing Structure for Big Image Data used for 3D Reconstruction . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: RGB-SpectralImaging, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 705-714. DOI: 10.5220/0005852207050714


in Bibtex Style

@conference{rgb-spectralimaging16,
author={Konstantinos Makantasis and Yannis Katsaros and Anastasios Doulamis and Matthaios Bimpas},
title={Online Indexing Structure for Big Image Data used for 3D Reconstruction},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: RGB-SpectralImaging, (VISIGRAPP 2016)},
year={2016},
pages={705-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005852207050714},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: RGB-SpectralImaging, (VISIGRAPP 2016)
TI - Online Indexing Structure for Big Image Data used for 3D Reconstruction
SN - 978-989-758-175-5
AU - Makantasis K.
AU - Katsaros Y.
AU - Doulamis A.
AU - Bimpas M.
PY - 2016
SP - 705
EP - 714
DO - 10.5220/0005852207050714