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Image re-ranking system based on closed frequent patterns

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Abstract

Text-based image retrieval is a popular and simple framework, which consists in using text annotations (e.g., image names, tags) to efficiently collect images relevant to a query word, from very large image collections. Even if the set of images retrieved using text annotations is noisy, it constitutes a reasonable initial set of images that can be considered as a bootstrap and improved further by analyzing image content. In this context, this paper introduces an approach for improving this initial set by re-ranking the so-obtained images, assuming that non-relevant images are scattered (i.e., they do not form clusters), unlike the relevant ones. More specifically, the approach consists in computing efficiently and on-the-fly closed frequent patterns, and in re-ranking images based on the number of patterns they contain. To do this, the paper introduces a simple but powerful new scoring function. Moreover, after the re-ranking process, we show how pattern mining techniques can also be applied for promoting diversity in the top-ranked images. The approach is validated on three different datasets for which state-of-the-art results are obtained.

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References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In VLDB, pp 487–499

  2. Bayardo Jr RJ (1998) Efficiently mining long patterns from databases. In ACM Sigmod Record, vol 27. ACM

  3. Ben-Haim N, Babenko B, S. Belongie (2006) Improving web-based image search via content based clustering. In CVPR Workshop

  4. Berg T, Forsyth D (2006) Animals on the web. In CVPR

  5. Berg TL, Berg AC (2009) Finding iconic images. In CVPR Workshop

  6. Fergus R, Fei-Fei L, Perona P, Zisserman A (2006) Learning object categories from google’s image search. In ICCV

  7. Fergus R, Perona P, Zisserman A (2004) A visual category filter for google images. In ECCV

  8. Fritz M, Schiele B (2006) Towards unsupervised discovery of visual categories. In DAGM

  9. Fritz M, Schiele B (2008) Decomposition, discovery and detection of visual categories using topic models. In CVPR

  10. Gionis A, Indyk P, Motwani R (1999) Similarity search in high dimensions via hashing. In Proceedings of the 25th international conference on very large data bases. VLDB

  11. Grangier D, Bengio S (2008) A discriminative kernel-based model to rank images from text queries. PAMI 30:1371–1384

  12. Grauman K, Darrell T (2005) The pyramid match kernel: discriminative classification with sets of image features. In ICCV

  13. Hsu WH, Kennedy LS, Chang S-F (2007) Reranking methods for visual search. Multimed IEEE 14:14–22

  14. Jégou H, Perronnin F, Douze M, Sánchez J, Pérez P, Schmid C (2012) Aggregating local image descriptors into compact codes. IEEE PAMI 34(9):1704–1716

    Article  Google Scholar 

  15. Jing Y, Baluja S (2008) Visualrank: applying pagerank to large-scale image search. PAMI 30:1877–1890

    Article  Google Scholar 

  16. Kennedy L, Naaman M (2008) Generating diverse and representative image search results for landmarks. ACM, In WWW

    Google Scholar 

  17. Krapac J, Allan M, Verbeek J, Jurie F (2010) Improving web-image search results using query-relative classifiers. In CVPR

  18. Ksibi A, Feki G, Ammar AB, Amar CB (2013) Effective diversification for ambiguous queries in social image retrieval. In CAIP (2), volume 8048 of lecture notes in computer science, Springer, pp 571–578

  19. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In CVPR

  20. Liu W, Jiang Y, Luo J, Chang S (2011) Noise resistant graph ranking for improved web image search. In CVPR

  21. Nowozin S, Tsuda K, Uno T, Kudo T, BakIr G (2007) Weighted substructure mining for image analysis. In CVPR

  22. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In ICDT

  23. Rajasekaran S, Reif JH (1989) Optimal and sublogarithmic time randomized parallel sorting algorithms. SIAM J Comput 18(3):594–607

    Article  MathSciNet  MATH  Google Scholar 

  24. Rioult F, Boulicaut J-F, Crémilleux B, Besson J (2003) Using transposition for pattern discovery from microarray data. In 8th ACM SIGMOD Workshop in DMKD

  25. Schroff F, Criminisi A, Zisserman A (2007) Harvesting image databases from the web. In ICCV

  26. Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In ICCV

  27. Su Y, Jurie F (2011) Visual word disambiguation by semantic contexts. In ICCV

  28. Thollard F, Quénot G (2013) Content-based re-ranking of text-based image search results. In ECIR

  29. Uno T, Asai T, Uchida Y, Arimura H (2004) An efficient algorithm for enumerating closed patterns in transaction databases. In DS

  30. van Leuken RH, Garcia L, Olivares X, van Zwol R (2009) Visual diversification of image search results. In Proceedings of the 18th international conference on World Wide Web, WWW ’09. New York, ACM, pp 341–350

  31. Vedaldi A, Fulkerson B (2008) VLFeat: an open and portable library of computer vision algorithms. http://www.vlfeat.org/

  32. Voravuthikunchai W, Crémilleux B, Jurie F (2012) Finding groups of duplicate images in very large dataset. In BMVC pp 1–12

  33. Voravuthikunchai W, Crémilleux B, Jurie F (2014) Histograms of pattern sets for image classification and object recognition. In CVPR

  34. Wallraven C Caputo B, Graf V (2003) Recognition with local features: the kernel recipe. In ICCV

  35. Wang J, Jiang Y-G, Chang SF (2009) Label diagnosis through self tuning for web image search. In CVPR

  36. Yang K, Wang M, Hua X-S, Zhang H-J (2010) Social image search with diverse relevance ranking. In Boll S, Tian Q, 0001 LZ, Zhang Z, Chen Y-PP, (eds) MMM, volume 5916 of lecture notes in computer science, springer, pp 174–184

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Acknowledgments

This work was partially funded by the QUAERO project supported by OSEO, French State agency for innovation.

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Authors and Affiliations

  1. GREYC - UMR6072 CNRS, ENSICAEN, University of Caen, Caen, France

    Winn Voravuthikunchai, Bruno Crémilleux & Frédéric Jurie

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  1. Winn Voravuthikunchai
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  2. Bruno Crémilleux
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  3. Frédéric Jurie
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Correspondence to Winn Voravuthikunchai.

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Voravuthikunchai, W., Crémilleux, B. & Jurie, F. Image re-ranking system based on closed frequent patterns. Int J Multimed Info Retr 3, 231–244 (2014). https://doi.org/10.1007/s13735-014-0066-8

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  • DOI: https://doi.org/10.1007/s13735-014-0066-8

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