Bag of Words - Quality Issues of Near-Duplicate Image Retrieval

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Published:
Jun 15, 2014
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Vol. 23 No. 1/2 (2014)
Section
Original research papers
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Mariusz Paradowski
Institute of Informatics, Wrocław University of Technology, Poland
Bartosz Broda
Institute of Informatics, Wrocław University of Technology, Poland
Mariusz Durak
Institute of Informatics, Wrocław University of Technology, Poland

DOI: https://doi.org/10.22630/MGV.2014.23.1.5
Keywords : spatial verification, vector space model, visual words, clustering
Abstract
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.

Article Details

How to Cite
Paradowski, M., Broda, B., & Durak, M. (2014). Bag of Words - Quality Issues of Near-Duplicate Image Retrieval. Machine Graphics and Vision, 23(1/2), 83–96. https://doi.org/10.22630/MGV.2014.23.1.5
References

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. (Crossref)

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.

C. Schmid and R. Mohr. Object recognition using local characterization and semi-local constraints. Technical report, INRIA, 1996.

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. (Crossref)

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. (Crossref)

K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. International Journal of Computer Vision, vol. 60, 2004, pp. 63–86. (Crossref)

D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, vol. 60, no. 2, 2004, pp. 91–110. (Crossref)

J. Zobel and M. Alistair. Inverted files for text search engines. ACM computing surveys (CSUR), vol. 38(2), 2006. (Crossref)

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.

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. (Crossref)

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. (Crossref)

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. (Crossref)

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.

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. (Crossref)

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. (Crossref)

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. (Crossref)

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. (Crossref)

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. (Crossref)

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