Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This paper addresses the problem of large scale near-duplicate image retrieval. Issues related to visual words dictionary generation are discussed. A new spatial verification routine is proposed. It incorporates neighborhood consistency, term weighting and it is integrated into the Bhattacharyya coefficient. The proposed approach reaches almost 10% higher retrieval quality, comparing to other recently reported state-of-the-art methods.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
83--96
Opis fizyczny
Bibliogr. 18 poz., tab., wykr.
Twórcy
autor
- Institute of Informatics, Wroclaw University of Technology, Poland
autor
- Institute of Informatics, Wroclaw University of Technology, Poland
autor
- Institute of Informatics, Wroclaw University of Technology, Poland
Bibliografia
- [1] G. Salton, A. Wong and C. S. Yang. A vector space model for automatic indexing. Communications of the ACM, vol. 18(11), 1975, pp. 613–620, 1975.
- [2] M. Fischler and R. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. 4th European Conference on Computer Vision (ECCV’96), Cambridge (UK), 1996, pp. 683–695, 1996.
- [3] C. Schmid and R. Mohr. Object recognition using local characterization and semi-local constraints. Technical report, INRIA, 1996.
- [4] C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5), 1997, pp. 530–535.
- [5] J. Sivic and A. Zisserman. Video google: a text retrieval approach to object matching in videos. Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV’03), vol. 2, 2003, pp. 1470–1477.
- [6] K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. International Journal of Computer Vision, vol. 60, 2004, pp. 63–86.
- [7] D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, vol. 60, no. 2, 2004, pp. 91–110.
- [8] J. Zobel and M. Alistair. Inverted files for text search engines. ACM computing surveys (CSUR), vol. 38(2), 2006.
- [9] S. H. Cha. Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences, vol. 1(4), 2007, pp. 300–307.
- [10] J. Philbin, O. Chum, M. Isard, J. Sivic and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. IEEE Conference on Computer Vision and Pattern Recognition, 2007.
- [11] J. Philbin, O. Chum, M. Isard, J. Sivic and A. Zisserman. Lost in quantization: improving particular object retrieval in large scale image databases. IEEE Conference on Computer Vision and Pattern Recognition, 2008.
- [12] O. Chum, J. Philbin and A. Zisserman. Near duplicate image detection: min-hash and tf-idf weighting. Proceedings of the British Machine Vision Conference, 2008, pp. 812–815.
- [13] R. Farivar, D. Rebolledo, E. Chan and R. H.Campbell. A parallel implementation of k-means clustering on GPUs. The 2008 International Conference on Parallel and Distributed Processing Techniques and Applications, 2008, pp. 340–345.
- [14] O. Chum, M. Perdoch and J. Matas. Geometric min-hashing: finding a (thick) needle in a haystack. IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 17–24.
- [15] H. Jegou, M. Douze and C. Schmid. Improving bag-of-features for large scale image search. International Journal of Computer Vision, vol. 87, 2010, pp. 316–336.
- [16] D.D. Yang and A. Sluzek. A low-dimensional local descriptor incorporating TPS warping for image matching. Image and Vision Computing, Vol. 28(8), August 2010, pp. 1184–1195.
- [17] M. Paradowski and A. Sluzek. Local keypoints and global affine geometry: triangles and ellipses for image fragment matching. Innovations in Intelligent Image Analysis (eds. H. Kwasnicka, L. Jain), Springer Verlag, vol. 339, 2011, pp. 195–224.
- [18] R. Arandjelovic and A. Zisserman. Three things everyone should know to improve object retrieval. IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2911–2918.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8244bc2f-06f9-4fb9-b9bf-b7aaeb0b121f