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Unsupervised Effectiveness Estimation Through Intersection of Ranking References

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Computer Analysis of Images and Patterns (CAIP 2019)

Abstract

Estimating the effectiveness of retrieval systems in unsupervised scenarios consists in a task of crucial relevance. By exploiting estimations which dot not require supervision, the retrieval results of many applications as rank aggregation and relevance feedback can be improved. In this paper, a novel approach for unsupervised effectiveness estimation is proposed based the intersection of ranking references at top-k positions of ranked lists. An experimental evaluation was conducted considering public datasets and different image features. The linear correlation between the proposed measure and the effectiveness evaluation measures was assessed, achieving high scores. In addition, the proposed measure was also evaluated jointly with rank aggregation methods, by assigning weights to ranked lists according to the effectiveness estimation of each feature.

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Notes

  1. 1.

    The constant e is set to 1.5 on the experimental evaluation.

References

  1. Arica, N., Vural, F.T.Y.: BAS: a perceptual shape descriptor based on the beam angle statistics. Pattern Recognit. Lett. 24(9–10), 1627–1639 (2003)

    Article  MATH  Google Scholar 

  2. Bai, S., Bai, X., Tian, Q., Latecki, L.J.: Regularized diffusion process on bidirectional context for object retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 41(5), 1213–1226 (2019)

    Article  Google Scholar 

  3. Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)

    Google Scholar 

  4. Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 613–622 (2001)

    Google Scholar 

  5. Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. In: ACM-SIAM Symposium on Discrete Algorithms, SODA 2003, pp. 28–36 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  6. Gopalan, R., Turaga, P., Chellappa, R.: Articulation-invariant representation of non-planar shapes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 286–299. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15558-1_21

    Chapter  Google Scholar 

  7. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 1997, pp. 762–768 (1997)

    Google Scholar 

  8. Jia, Q., Tian, X.: Query difficulty estimation via relevance prediction for image retrieval. Signal Process. 110(C), 232–243 (2015)

    Article  Google Scholar 

  9. Kovalev, V., Volmer, S.: Color co-occurence descriptors for querying-by-example. In: International Conference on Multimedia Modeling, p. 32 (1998)

    Google Scholar 

  10. Latecki, L.J., Lakmper, R., Eckhardt, U.: Shape descriptors for non-rigid shapes with a single closed contour. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000, pp. 424–429 (2000)

    Google Scholar 

  11. Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 2(1), 1–19 (2006)

    Article  Google Scholar 

  12. Ling, H., Jacobs, D.W.: Shape classification using the inner-distance. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 286–299 (2007)

    Article  Google Scholar 

  13. Ling, H., Yang, X., Latecki, L.J.: Balancing deformability and discriminability for shape matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 411–424. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15558-1_30

    Chapter  Google Scholar 

  14. Manjunath, B., Ohm, J.R., Vasudevan, V., Yamada, A.: Color and texture descriptors. IEEE Trans. Circ. Syst. Video Technol. 11(6), 703–715 (2001)

    Article  Google Scholar 

  15. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  16. Pedronette, D.C.G., da Silva Torres, R.: Unsupervised effectiveness estimation for image retrieval using reciprocal rank information. In: 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 321–328 (2015)

    Google Scholar 

  17. Pedronette, D.C.G., Penatti, O.A.B., Calumby, R.T., da Silva Torres, R.: Unsupervised distance learning by reciprocal kNN distance for image retrieval. In: International Conference on Multimedia Retrieval, ICMR 2014 (2014)

    Google Scholar 

  18. Pedronette, D.C.G., Penatti, O.A., da Silva Torres, R.: Unsupervised manifold learning using reciprocal kNN graphs in image re-ranking and rank aggregation tasks. Image Vis. Comput. 32(2), 120–130 (2014)

    Article  Google Scholar 

  19. Pedronette, D.C.G., da Silva Torres, R.: Shape retrieval using contour features and distance optmization. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP 2010, vol. 1, pp. 197–202 (2010)

    Google Scholar 

  20. Pedronette, D.C.G., da Silva Torres, R.: Image re-ranking and rank aggregation based on similarity of ranked lists. Pattern Recognit. 46(8), 2350–2360 (2013)

    Article  Google Scholar 

  21. Pedronette, D.C.G., Gonçalves, F.M.F., Guilherme, I.R.: Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks. Pattern Recognit. 75, 161–174 (2018)

    Article  Google Scholar 

  22. Piras, L., Giacinto, G.: Information fusion in content based image retrieval: a comprehensive overview. Inf. Fusion 37(Supplement C), 50–60 (2017)

    Article  Google Scholar 

  23. da Silva Torres, R., Falcão, A.X.: Content-based image retrieval: theory and applications. Revista de Informática Teórica e Aplicada 13(2), 161–185 (2006)

    Google Scholar 

  24. da Silva Torres, R., Falcão, A.X.: Contour salience descriptors for effective image retrieval and analysis. Image Vis. Comput. 25(1), 3–13 (2007)

    Article  Google Scholar 

  25. Stehling, R.O., Nascimento, M.A., Falcão, A.X.: A compact and efficient image retrieval approach based on border/interior pixel classification. In: ACM Conference on Information and Knowledge Management, CIKM 2002, pp. 102–109 (2002)

    Google Scholar 

  26. Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)

    Article  Google Scholar 

  27. Tao, B., Dickinson, B.W.: Texture recognition and image retrieval using gradient indexing. J. Vis. Comun. Image Represent. 11(3), 327–342 (2000)

    Article  Google Scholar 

  28. van de Weijer, J., Schmid, C.: Coloring local feature extraction. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part II. LNCS, vol. 3952, pp. 334–348. Springer, Heidelberg (2006). https://doi.org/10.1007/11744047_26

    Chapter  Google Scholar 

  29. Xing, X., Zhang, Y., Han, M.: Query difficulty prediction for contextual image retrieval. In: Gurrin, C., et al. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 581–585. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12275-0_52

    Chapter  Google Scholar 

  30. Zheng, L., Wang, S., Tian, L., He, F., Liu, Z., Tian, Q.: Query-adaptive late fusion for image search and person re-identification. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1741–1750, June 2015

    Google Scholar 

Download references

Acknowledgments

The authors are grateful to the São Paulo Research Foundation - FAPESP (grants #2017/02091-4,#2018/15597-6, #2017/25908-6, and #2019/04754-6), the Brazilian National Council for Scientific and Technological Development - CNPq (grant #308194/2017-9), and Petrobras (grant #2017/00285-6).

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Correspondence to Daniel Carlos Guimarães Pedronette .

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Presotto, J.G.C., Valem, L.P., Pedronette, D.C.G. (2019). Unsupervised Effectiveness Estimation Through Intersection of Ranking References. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-29891-3_21

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  • Online ISBN: 978-3-030-29891-3

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