Multimedia Systems

, Volume 12, Issue 1, pp 14–26 | Cite as

Improved AdaBoost-based image retrieval with relevance feedback via paired feature learning

Regular Paper

Abstract

Boost learning algorithm, such as AdaBoost, has been widely used in a variety of applications in multimedia and computer vision. Relevance feedback-based image retrieval has been formulated as a classification problem with a small number of training samples. Several machine learning techniques have been applied to this problem recently. In this paper, we propose a novel paired feature AdaBoost learning system for relevance feedback-based image retrieval. To facilitate density estimation in our feature learning method, we propose an ID3-like balance tree quantization method to preserve most discriminative information. By using paired feature combination, we map all training samples obtained in the relevance feedback process onto paired feature spaces and employ the AdaBoost algorithm to select a few feature pairs with best discrimination capabilities in the corresponding paired feature spaces. In the AdaBoost algorithm, we employ Bayesian classification to replace the traditional binary weak classifiers to enhance their classification power, thus producing a stronger classifier. Experimental results on content-based image retrieval (CBIR) show superior performance of the proposed system compared to some previous methods.

Keywords

AdaBoost Image retrieval Relevance feedback Paired feature learning 

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References

  1. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  2. Zhou, X.S., Rui, Y., Huang, T.S.: Water-filling: a novel way for image structural feature extraction. Proc. Int. Conf. Image Process. 2, 24–28 (1999)Google Scholar
  3. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8) 837–842 (1996)CrossRefGoogle Scholar
  4. Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E.H., Petkovic, D., Yanker, P., Faloutsos, C., Taubin, G.: The QBIC project: querying images by content using color, texture and shape. In: Proceedings of the SPIE Storage and Retrieval for Image and Video Databases (1993)Google Scholar
  5. Rui, Y., Huang, T.S., Chang, S.F.: Image retrieval: current techniques, promising directions and open issues. J. Vis. Commun. Image Represent. 10(4) 39–62 (1999)CrossRefGoogle Scholar
  6. Ciocca, G., Gagliardi, I., Schettini, R.: Quicklook2: an integrated multimedia system. Int. J. Vis. Lang. Comput. (Special Issue on Multimedia Databases and Image Communication) 12, 81–103 (2001)CrossRefGoogle Scholar
  7. Sclaroff, S., Taycher, L., Cascia, M.L.: Imagerover: a content-based image browser for the world wide web. In:Proceedings of IEEE Workshop on Content-based Access of Image and Video Libraries, pp. 2–9 (1997)Google Scholar
  8. Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)MATHGoogle Scholar
  9. Rui, Y., Huang, T.S., Mehrotra, S.: Content-based image retrieval with relevance feedback in MARS. Proc. Int. Conf. Image Process. 2, 815–818 (1997)CrossRefGoogle Scholar
  10. Chen, Y., Zhou, X., Huang, T.S.: One-class SVM for learning in image retrieval. Proc. Int. Conf. Image Process. 1, 34–37 (2001)Google Scholar
  11. Tieu, K., Viola, P.: Boosting Image Retrieval. Proc. IEEE Conf. Comp. Vis. Pattern Recogn. 1 228–235 (2000)Google Scholar
  12. Ishikawa, Y., Subramanya, R., Faloutsos, C.: MindReader: Querying databases through multiple examples. In: Proceedings of the 24th International Conference Very Large Data Bases, pp. 218–227 (1998)Google Scholar
  13. Rui, Y., Huang, T.: Optimizing learning in image retrieval. Proc. IEEE Conf. Comp. Vis. Pattern Recogn. 1, 236–243 (2000)Google Scholar
  14. Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The Bayesian image retrieval system, PicHunter: Theroy, implementation, and psychological experiments. IEEE Trans. Image Process. 9, 20–37 (2000)CrossRefGoogle Scholar
  15. Kushki, A., Androutsos, P., Plataniotis, K.N., Venetsanopoulos, A.N.: Query feedback for iteractive image retrieval. IEEE Trans. Circ. Syst. Video Technol. 14(5), 644–655 (2004)CrossRefGoogle Scholar
  16. Kim, D.-H., Chung, C.-W.: QCluster: relevance feedback using adaptive clustering for content-based image retrieval. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 599–610 (2003)Google Scholar
  17. Tao, D., Tang, X.: Random Sampling Based SVM for Relevance Feedback Image Retrieval. Proc. IEEE Int. Conf. Comp. Vis. Pattern Recogn. 2, 647–652 (2004)Google Scholar
  18. Doulamis, N.D., Doulamis, A.D., Varvarigou, T.A.: Adaptive algorithms for interactive multimedia. Multimedia, IEEE 10(4), 38–47 (2003)CrossRefGoogle Scholar
  19. Doulamis, A., Doulamis, N.: Performance evaluation of euclidean/correlation-based relevance feedback algorithms in content-based image retrieval systems. Proc. Int. Conf. Image Process. 1, 737–740 (2003)Google Scholar
  20. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the European Conference on Computational Learning Theory, pp. 23–37, (1995)Google Scholar
  21. Zhou, X.S., Huang, T.S.: Small sample learning during multimedia retrieval using BiasMap. Proc. IEEE Conf. Comp. Vis. Pattern Recogn. 1, 11–17 (2001)Google Scholar
  22. Zhou, X.S., Garg, A., Huang, T.S.: A discussion of nonlinear variants of biased discriminants for interactive image retrieval. In: Proceedings of the International Conference on Image and Video Retrieval, pp. 353–364 (2004)Google Scholar
  23. Zhou, X.S., Grag, A., Huang, T.S.: Nonlinear variants of biased discriminants for interactive image retrieval. IEE Proc. Vis. Image Sig. Process. 152(6), 927–936 (2005)CrossRefGoogle Scholar
  24. Viola, P., Jones, M.: Rapid object detection uaing boosted cascade of simple features. Proc. IEEE Conf. Comp. Vis. Pattern Recogn. 1, 511–518 (2001)Google Scholar
  25. Ratan, A.L., Maron, O., Grimson, W.E.L., Lozano-Perez, T.: A framework for leaning query concepts in image classification. Proc. IEEE Conf. Comp. Vis. Pattern Recogn. 1, 23–25 (1999)Google Scholar
  26. Belongie, S., Carson, C., Greenspan, H., Malik, J.: Recognition of images in large databases using color and texture. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1026–1038 (2002)CrossRefGoogle Scholar
  27. Gong, Y., Zhang, H.J., Chuan, H.C., Sakauchi, M.: An image database system with content capturing and fast image indexing abilities. In: Proceedings of the IEEE International Conference on Multimedia Computing and Systems, pp. 121–130 (1994)Google Scholar
  28. Ma, W.Y., Manjunath, B.S.: NETRA: A toolbox for navigating large image databases. Proc. IEEE Int. Conf. Image Process. 1, 568–571 (1997)CrossRefGoogle Scholar
  29. Minka, T.P., Picard, R.W.: Interactive learning using a society of models. Pattern Recogn. 30, 565–581 (1997)CrossRefGoogle Scholar
  30. Wood, M.E., Campbell, N.W., Thomas, B.T.: Iterative refinement by relevant feedback in content based digital image retrieval. In: Proceedings of the International Conference on Multimedia, pp. 13–20 (1998)Google Scholar
  31. Jing, F., Li, M.J., Zhang, H.J., Zhang, B.: Learning region weighting from relevance feedback in image retrieval. Proc. IEEE Int. Conf. Acoust. Speech Sign. Process. 4, 4088–4091 (2002)Google Scholar
  32. Jing, F., Li, M.J., Zhang, H.J., Zhang, B.: Region-based relevance feedback in image retrieval. Proc. IEEE Int. Symp. Circ. Syst. 4, 145–148 (2002)Google Scholar
  33. Jing, F., Li, M.J., Zhang, H.J., Zhang, B.: Support vector machines for region-based image retrieval. Proc. IEEE Int. Conf. Multimedia Expo. 2, 21–24 (2003)CrossRefGoogle Scholar
  34. Natsev, A., Rastogi, R., Shim, K.: WALRUS: A similarity retrieval algorithm for image databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 395–406 (1999)Google Scholar
  35. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23, 947–963 (2001)CrossRefGoogle Scholar
  36. Wang, T., Rui, Y., Sun, J.G.: Constraint-based region matching for image retrieval. In: Proceedings of the International Conference on Computer Vision, pp. 37–45 (2004)Google Scholar
  37. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. Annal. Stat. 28(2), 337–374 (2000)CrossRefMATHMathSciNetGoogle Scholar
  38. Li, S.Z., Zhang, Z.: FloatBoost Learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1112–1123 (2004)CrossRefPubMedGoogle Scholar
  39. He, J., Li, M., Zhang, H.-J., Zhang, C.: W-Boost and its application to web image classification. Proc. IEEE Int. Conf. Pattern Recogn. 1, 148–151 (2004)Google Scholar
  40. Liu, C., Shum, H.-Y.: Kullback-Leibler Boosting. Proc. IEEE Conf. Comp. Vis. Pattern Recogn. 1, 587–594 (2003)Google Scholar
  41. Jing, F., Li, M., Zhang, H.J., Zhang, B.: Relevance feedback in region-based image retrieval. Proc. Int. Conf. Pattern Recogn. 14, 672–681 (2002)Google Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchu 300Taiwan

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