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Semantic class discriminant projection for image retrieval with relevance feedback

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Abstract

With the user’s feedback, projections are often used to reduce dimension and enhance class discrimination. The existing projections either use only the global euclidean structure or refer to the local manifold structure. However, global statistics such as variance (ie the method using the global euclidean structure) is difficult to estimate when there are not enough training samples. As for the methods that use the local manifold structure, the class discriminant is limited. In this paper, a Semantic Class Discriminant Projection (SCDP) is proposed for enhancing the performance of content-based image retrieval schemas with relevance feedback. SCDP can take advantage of the local geometry information of labeled and unlabeled images to learn a semantic subspace, and it obtains the most important properties of the subspaces to enhance classification. The experimental results performed on the two benchmark datasets have confirmed the superiority of the proposed method.

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References

  1. Cai D, Xiaofei H, Jiawei H (2007) Semi-supervised discriminant analysis. Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on IEEE, pp 1-7

  2. Carneiro G, Chan AB, Moreno PJ, Vasconcelos N (2007) Supervised Learning of Semantic Classes for Image Annotation and Retrieval. IEEE Trans Pattern Anal Mach Intell 29(3):394–410

    Article  Google Scholar 

  3. Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60

    Article  Google Scholar 

  4. Ding C, Zhang L (2015) Double adjacency graphs-based discriminant neighborhood embedding. Pattern Recogn 48:1734–1742

    Article  Google Scholar 

  5. Dornaika F, El Traboulsi Y (2016) Learning flexible graph-based semi-supervised embedding. IEEE Trans Cybern 46(1):206–218

  6. Duda RO, Hart PE, Stork DG (2000) Pattern Classification, (2nd ed). Wiley-Interscience, New York, pp 688

  7. Gao Q et al (2015) A novel semi-supervised learning for face recognition. Neurocomputing 152(2015):69–76

    Article  Google Scholar 

  8. Geng X, Zhan DC, Zhou ZH (2005) Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Trans Syst Man Cybern B Cybern 35(6):1098–1107

    Article  Google Scholar 

  9. Gou J, Yang Y, Yi Z, Lv J, Mao Q, Zhan Y (2020) Discriminative globality and locality preserving graph embedding for dimensionality reduction. Expert Syst Appl 144:113079

    Article  Google Scholar 

  10. He XF, Niyogi P (2003) Locality preserving projections. In: Proc. Adv Neural Inf Proces Syst, pp. 153–160

  11. He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. In: Proc IEEE Int Conf Comput Vis (ICCV), pp. 1208–1213

  12. He X, Cai D, Han J (2008) Learning a maximum margin subspace for image retrieval. IEEE Trans Knowl Data Eng 20(2):189–201

    Article  Google Scholar 

  13. Huijsmans DP, Sebe N (2005) How to Complete Performance Graphs in Content-Based Image Retrieval: Add Generality and Normalize Scope. IEEE Trans Pattern Anal Mach Intell 27(2):245–251

    Article  Google Scholar 

  14. Jolliffe IT (2002) Principal Component Analysis, 2nd edn. Springer, New-York

    MATH  Google Scholar 

  15. Lai Z, Bao J, Kong H. et al. (2020) Discriminative low-rank projection for robust subspace learning. Int J Mach Learn Cyber 11:2247–2260

  16. Lin Y-Y, Liu T-L, Chen H-T (2005) Semantic Manifold Learning for Image Retrieval. Proc. 13th Ann. ACM Int’l Conf. Multimedia (Multimedia ‘05), pp 24–31

  17. Liu L, Yu M, Shao L (2015) Multiview alignment hashing for efficient image search. IEEE Trans Image Process 24(3):956–966

    Article  MathSciNet  Google Scholar 

  18. Liu Z, Liu G, Zhang L, Pu J (2020a) Linear regression classification steered discriminative projection for dimension reduction. Multimed Tools Appl 79:11993–12005

  19. Liu Z, Liu G, Zhang L, Pu J (2020b) Linear regression classification steered discriminative projection for dimension reduction. Multimed Tools Appl 79(17):11993–12005

    Article  Google Scholar 

  20. Martinez AM, Kak AC (2001) Pca versus lda. Pattern Analysis and Machine Intelligence 23:228–233

    Article  Google Scholar 

  21. Roweis ST (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  22. Sathiamoorthy S, Natarajan M (2020) An efficient content-based image retrieval using enhanced multi-trend structure descriptor. SN Appl Sci 2:217

    Article  Google Scholar 

  23. Shao L, Zhu F, Li X (2015) Transfer learning for visual categorization: A survey. IEEE Trans Neural Netw Learn Syst 26(5):1019–1034

    Article  MathSciNet  Google Scholar 

  24. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380

    Article  Google Scholar 

  25. Song Y et al (2008) A unified framework for semi-supervised dimensionality reduction. Pattern Recognit 41.9:2789–2799

    Article  Google Scholar 

  26. Swets DL, Weng J (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8)

  27. Tao D, Tang X, Li X, Wu X (2006a) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088–1099

    Article  Google Scholar 

  28. Tao D, Tang X, Li X, Rui Y (2006b) Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm. IEEE Trans Multimedia 8(4):716–727

    Article  Google Scholar 

  29. Tao Y, Yang J, Gui W (2018) Robust l2,1 norm-based sparse dictionary coding regularization of homogenous and heterogenous graph embeddings for image classifications. Neural Process Lett 47(3):1149–1175

    Article  Google Scholar 

  30. Vlachos M, Domeniconi C, Gunopulos D, Kollios G, Koudas N (2002) Non-linear dimensionality reduction techniques for classification and visualization. In: Proceedings of ACM Int. Conf. Knowl. Discovery Data Mining, pp 645–651

  31. Wang JZ, Li J, Wiederhold G (2001) SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963

    Article  Google Scholar 

  32. Wong WK, Zhao HT (2012) Supervised optimal locality preserving projection. Pattern Recogn 45:186–197

    Article  Google Scholar 

  33. Xu Y, Zhong AN, Yang J, Zhang D (2010) Lpp solution schemes for use with face recognition. Pattern Recogn 43:4165–4176

    Article  Google Scholar 

  34. Yan SC, Xu D, Zhang BY, Zhang HJ, Yang Q (2005) Graph embedding: A general framework for dimensionality reduction. Comput Vis Pattern Recognit 2:830–837

    Google Scholar 

  35. Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  36. Zhang W, Xue XY, Lu H, Guo YF (2006) Discriminant neighborhood embedding for classification. Pattern Recogn 39:2240–2243

    Article  Google Scholar 

  37. Zhang L, Shum HPH, Shao L (2016) Discriminative semantic subspace analysis for relevance feedback. IEEE Trans Image Process 25(3):1275–1287

    Article  MathSciNet  Google Scholar 

  38. Zhao HT, Sun SY, Jing ZL, Yang JY (2006) Local structure-based supervised feature extraction. Pattern Recogn 39:1546–1550

    Article  Google Scholar 

Download references

Acknowledgments

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2020.10”.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Thuyloi University, 175 Tayson, Hanoi, Vietnam

    Quynh Nguyen Huu & Dung Cu Viet

  2. Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam

    Quynh Dao Thi Thuy

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  1. Quynh Nguyen Huu
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  2. Dung Cu Viet
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  3. Quynh Dao Thi Thuy
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Correspondence to Quynh Dao Thi Thuy.

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Huu, Q.N., Viet, D.C. & Thuy, Q.D.T. Semantic class discriminant projection for image retrieval with relevance feedback. Multimed Tools Appl 80, 15351–15376 (2021). https://doi.org/10.1007/s11042-020-10400-y

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