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Content-based image retrieval using student’s t-mixture model and constrained multiview nonnegative matrix factorization

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Abstract

The expensive and time-consuming effort required for archiving images is the main motive for developing an effective retrieval system. This paper addresses a competitive scheme for Content-Based Image Retrieval (CBIR) based on a constrained multiview Nonnegative Matrix Factorization (NMF) that has the ability to generate a sparse representation. The scheme blends multiple visual features, which can together reflect the content of images in terms of similarity metrics and the Frobenius norm. Then, the proposed method constructs a similarity-preserving matrix factorization via an improved NMF, where the structural constraint, L 1/2-sparse constraint and farness-preserving constraint are integrated into the objective function of conventional NMF. In this way, the structure and content of high-dimensional feature data source can be preserved in low-dimensional space. Another critical part of the proposed system is to establish Student’s t-Mixture Model (SMM) based on a Markov Random Field (MRF), which can best manipulate the clustering of sparse representations according to the statistical properties of the image features. With this method, the task of image retrieval of the whole dataset is reduced to a nearest-neighbour search in a specific category containing the query image. Convergence of the proposed update rule, investigated in this study, is also verified by numerical simulations. Lastly, we conduct experiments on public datasets to compare the performance of the proposed algorithm with existing works in terms of Precision and Recall Rates. The encouraging results indicate the effectiveness of the proposed technique.

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Notes

  1. http://www.vision.caltech.edu/Image_Datasets/Caltech101

  2. http://wang.ist.psu.edu/docs/related/

  3. http://imagedatabase.cs.washington.edu/

  4. http://www.fil.ion.ucl.ac.uk/spm

References

  1. Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. Lect Notes Comput Sci 3021:469–481

    Article  MATH  Google Scholar 

  2. Amin T, Zeytinoglu M, Guan L (2007) Application of Laplacian mixture model to image and video retrieval. IEEE Trans Multimedia 9(7):1416–1429

    Article  Google Scholar 

  3. An L, Zou CJ, Zhang LY, Denney B (2016) Scalable attribute-driven face image retrieval. Neurocomputing 172:215–224

    Article  Google Scholar 

  4. Babaee M, Bahmanyar R, Rigoll G, Datcu M (2014) Farness preserving non-negative matrix factorization. In: ICIP’14: International Conference on Image Processing 3023–3027

  5. Babaee M, Tsoukalas S, Babaee M, Rigoll R, Datcu M (2016) Discriminative nonnegative matrix factorization for dimensionality reduction. Neurocomputing 173:212–223

    Article  Google Scholar 

  6. Boyd SP, Vandenberghe L (2004) Convex optimization. Cambridge University Press, United Kingdom

    Book  MATH  Google Scholar 

  7. Cai D, He X, Han J, Huang TS (2011) Graph regularized non-negative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548–1560

    Article  Google Scholar 

  8. Cox TE, Cox MA (2010) Multidimensional scaling. CRC Press, United States

    MATH  Google Scholar 

  9. Cui S, Datcu M (2015) Comparison of Kullback-Leibler divergence approximation methods between Gaussian mixture models for satellite image retrieval. IEEE Geoscience and Remote Sensing Symposium 3719–3722

  10. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR’05: IEEE Conference on Computer Vision and Pattern Recognition, San Diego 1 (12): 886-893

  11. Deselaers T, Keysers D, Ney H (2008) Features for image retrieval: an experimental comparison. Inf Retr 11(2):77–107

    Article  Google Scholar 

  12. Feng L, Bhanu B (2016) Semantic concept co-occurrence patterns for image annotation and retrieval. IEEE Trans Pattern Anal Mach Intell 38(4):785–799

    Article  Google Scholar 

  13. Flusser J, Zitova B, Suk T (2009) Moments and moment invariants in pattern recognition. Wiley, New York

    Book  MATH  Google Scholar 

  14. Gertheiss J, Tutz G (2009) Feature selection and weighting by nearest neighbor ensembles. Chemom Intell Lab Syst 99(2):30–38

    Article  Google Scholar 

  15. Gillis N, Kuang D, Park H (2015) Hierarchical clustering of hyperspectral images using rank-two nonnegative matrix factorization. IEEE Trans Geosci Remote Sens 53(4):2066–2078

    Article  Google Scholar 

  16. Greenspan H, Pinhas AT (2007) Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Trans Info Technol Biomed 11(2):190–202

    Article  Google Scholar 

  17. Han J, Ma KK (2002) Fuzzy color histogram and its use in color image retrieval. IEEE Trans Image Process 11(8):944–952

    Article  Google Scholar 

  18. Hyvärinen A (2001) Independent component analysis. Neural Comput Sur 4:60–83

    MATH  Google Scholar 

  19. Kim H, Park H (2008) Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J Matrix Anal Appl 30(2):713–730

    Article  MathSciNet  MATH  Google Scholar 

  20. Klema VC, Laub AJ (1980) The singular value decomposition: Its computation and some applications. IEEE Trans Autom Control 25(2):164–176

    Article  MathSciNet  MATH  Google Scholar 

  21. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401:788–791

    Article  MATH  Google Scholar 

  22. Liu H, Wu Z, Cai D, Huang TS (2012) Constrained nonnegative matrix factorization for image representation. IEEE Trans Softw Eng 34(7):1299–1311

    Google Scholar 

  23. Liu J, Wang C, Gao J, Han J (2013) Multi-view clustering via joint nonnegative matrix factorization. In: SDM’13: Proceeding of the 2013 SIAM International Conference on Data Mining 252–260

  24. 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 

  25. Lowe DG (2004) Distinctive image features from scale invariant key points. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  26. Marakakis A, Galatsanos N, Likas A, Stafylopatis A (2009) Probabilistic relevance feedback approach for content-based image retrieval based on Gaussian mixture models. IET Image Process 3(1):10–25

    Article  Google Scholar 

  27. Mittal A, Sofat S (2013) A novel color coherence vector based obstacle detection algorithm for textured environments. Int J Comput Theory Eng 5(1):81–84

    Article  Google Scholar 

  28. Nguyen TM, Jonathan Wu QM (2013) Fast and robust spatially constrained Gaussian mixture model for image segmentation. IEEE Trans Circuits Syst Video Technol 23(4):621–635

    Article  Google Scholar 

  29. Nguyen TM, Jonathan Wu QM (2014) Bounded asymmetrical Student’s-t mixture model. IEEE Trans Cybern 44(6):857–869

    Article  Google Scholar 

  30. Oliva A, Torralba A (2001) Modeling the shape of the scene: A holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  31. Peel D, McLachlan G (2000) Robust mixture modeling using the t-distribution. Stat Comput 10:335–344

    Article  Google Scholar 

  32. Piatek ML, Smolka B (2013) Color image retrieval based on spatio-chromatic multichannel Gaussian mixture modelling. In: ISPA’13: 8th International Symposium on Image and Signal Processing and Analysis 130–135

  33. Qi SY, Luo YP (2016) Object retrieval with image graph traversal-based re-ranking. Signal Process Image Commun 41:101–114

    Article  Google Scholar 

  34. Qian Y, Jia S, Zhou J, Robles-Kelly A (2011) Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization. IEEE Trans Geosci Remote Sens 49(11):4282–4297

    Article  Google Scholar 

  35. Rajabi R, Ghassemian H (2015) Spectral unmixing of hyperspectra imagery using multilayer NMF. IEEE Geosci Remote Sens Lett 12(1):38–42

    Article  Google Scholar 

  36. Shunfeng C, Michael P (2012) Using cross-validation for model parameter selection of sequential probability ratio test. Expert Syst Appl 39:8467–8473

    Article  Google Scholar 

  37. Wang Z, Feng Y, Qi T, Yang X, Zhang JJ (2016) Adaptive multi-view feature selection for human motion retrieval. Signal Process 120:691–701

    Article  Google Scholar 

  38. Wang W, Qian Y, Tang YY (2016) Hypergraph-regularized sparse NMF for hyperspectral unmixing. IEEE J Sel Top Appl Earth Obs Remote Sens 9(2):681–694

    Article  Google Scholar 

  39. Xia T, Tao D, Mei T, Zhang YD (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybern B Cybern 40(6):1438–1446

    Article  Google Scholar 

  40. Xu Z, Chang X, Xu F, Zhang H (2012) L1/2 regularization: A thresholding representation theory and a fast solver. IEEE Trans Neural Netw Learn Syst 23(7):1013–1027

    Article  Google Scholar 

  41. Yang WH, Liu GQ, Zhang L, Chen EH (2012) Multi-view learning with batch mode active selection for image retrieval. In: ICPR’12: 21st International Conference on Pattern Recognition 979–982

  42. Yang SY, Zhang XT, Yao YG, Cheng SQ, Jiao LC (2015) Geometric nonnegative matrix factorization (GNMF) for hyperspectral unmixing. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2696–2703

    Article  Google Scholar 

  43. Yeung KY, Ruzzo WL (2001) Principal component analysis for clustering gene expression data. Bioinformatics 17(9):763–774

    Article  Google Scholar 

  44. Zeng S, Huang R, Wang HB, Kang Z (2016) Image retrieval using spatiograms of colors quantized by Gaussian mixture models. Neurocomputing 171:673–684

    Article  Google Scholar 

  45. Zhu HQ, Liu M, Ji H, Li Y (2010) Combined invariants to blur and rotation using Zernike moment descriptors. Pattern Anal Applic 13:309–319

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper, This work was supported by the National Nature Science Foundation of China under Grant 61371150.

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Authors and Affiliations

  1. School of Information Science & Engineering, East China University of Science and Technology, No. 130 Mei Long Road, Shanghai, 200237, China

    Hongqing Zhu & Qunyi Xie

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  1. Hongqing Zhu
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  2. Qunyi Xie
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Corresponding author

Correspondence to Hongqing Zhu.

Appendix

Appendix

In this appendix, we provide the implementation details of each part shown in Fig. 1.

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Zhu, H., Xie, Q. Content-based image retrieval using student’s t-mixture model and constrained multiview nonnegative matrix factorization. Multimed Tools Appl 77, 14207–14239 (2018). https://doi.org/10.1007/s11042-017-5026-x

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  • DOI: https://doi.org/10.1007/s11042-017-5026-x

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