Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Sparse structure regularized ranking

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Agichtein E, Brill E, Dumais S (2006) Improving web search ranking by incorporating user behavior information, pp 19–26

  2. Ahonen T, Hadid A, Pietikäinen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  Google Scholar 

  3. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202

    Article  MATH  MathSciNet  Google Scholar 

  4. Bian W, Tao D (2010) Biased discriminant euclidean embedding for content-based image retrieval. IEEE Trans Image Process 19(2):545–554

    Article  MathSciNet  Google Scholar 

  5. Bober M (2001) Mpeg-7 visual shape descriptors. IEEE Trans Circ Syst Video Tech 11(6):716–719

    Article  Google Scholar 

  6. Breu H, Gil J, Kirkpatrick D, Werman M (1995) Linear time euclidean distance transform algorithms. IEEE Trans Pattern Anal Mach Intell 17(5):529–533

    Article  Google Scholar 

  7. Clausi D, Ed Jernigan M (2000) Designing gabor filters for optimal texture separability. Pattern Recog 33(11):1835–1849

    Article  Google Scholar 

  8. Cook N (2007) Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115(7):928–935

    Article  Google Scholar 

  9. Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves, pp 233–240

  10. De Maesschalck R, Jouan-Rimbaud D, Massart D (2000) The mahalanobis distance. Chemom Intell Lab Syst 50(1):1–18

    Article  Google Scholar 

  11. Ding K, Liu Y (2013) A probabilistic 3d model retrieval system using sphere image. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 7724 LNCS(PART), vol 1, pp 536–547

  12. Euzenat J (2007) Semantic precision and recall for ontology alignment evaluation, pp 348–353

  13. Feng DD, Siu WC, Zhang HJ (2003) Multimedia information retrieval and management: technological fundamentals and applications. Springer

  14. Forti M, Tesi A (1995) New conditions for global stability of neural networks with application to linear and quadratic programming problems. IEEE Trans Circuits Syst I: Fundam Theory Appl 42(7):354–366

    Article  MATH  MathSciNet  Google Scholar 

  15. Gao X, Xiao B, Tao D, Li X (2008) Image categorization: graph edit distance + edge direction histogram. Pattern Recog 41(10):3179–3191

    Article  MATH  Google Scholar 

  16. Grigorescu S, Petkov N, Kruizinga P (2002) Comparison of texture features based on gabor filters. IEEE Trans Image Process 11(10):1160–1167

    Article  MathSciNet  Google Scholar 

  17. Hand D, Till R (2001) A simple generalisation of the area under the roc curve for multiple class classification problems. Mach Learn 45(2):171–186

    Article  MATH  Google Scholar 

  18. Hanley J, McNeil B (1982) The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1):29–36

    Article  Google Scholar 

  19. Haveliwala T (2003) Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans Knowl Data Eng 15(4):784–796

    Article  Google Scholar 

  20. He R, Hu BG, Zheng WS, Guo Y (2010) Two-stage sparse representation for robust recognition on large-scale database. In: AAAI, vol 10, pp 1–1

  21. Hiremath P, Pujari J (2007) Content based image retrieval using color, texture and shape features, pp 780–784

  22. Hotho A, Jäschke R, Schmilz C, Stumme G (2006) Information retrieval in folksonomies: search and ranking. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 4011 LNCS, pp 411–426

  23. Huang Y, Powers R, Montelione G (2005) Protein nmr recall, precision, and f-measure scores (rpf scores): Structure quality assessment measures based on information retrieval statistics. J Am Chem Soc 127(6):1665–1674

    Article  Google Scholar 

  24. Jain A, Farrokhnia F (1991) Unsupervised texture segmentation using gabor filters. Pattern Recog 24(12):1167–1186

    Article  Google Scholar 

  25. Kapela R, Rybarczyk A (2007) Real-time shape description system based on mpeg-7 descriptors. J Syst Archit 53(9):602–618

    Article  Google Scholar 

  26. Kim JH, Seo YH, Kim DW, Yoo JS (2011) Stereoscopic conversion of monoscopic video using edge direction histogram. Int J Innov Comput Inf Control 7(11):6289–6300

    Google Scholar 

  27. Kim YW, Oh IS (2004) Watermarking text document images using edge direction histograms. Pattern Recog Lett 25(11):1243–1251

    Article  Google Scholar 

  28. Ma Z, Nie F, Yang Y, Uijlings J, Sebe N (2012) Web image annotation via subspace-sparsity collaborated feature selection. IEEE Trans Multimedia 14(4 PART1):1021–1030

    Article  Google Scholar 

  29. Mangai M, Gounden N (2013) Subspace-based clustering and retrieval of 3-d objects. Comput Electr Eng 39(3):809–817

    Article  Google Scholar 

  30. Momoh J, El-Hawary M, Adapa R (1999) A review of selected optimal power flow literature to 1993 part i: nonlinear and quadratic programming approaches. IEEE Trans Power Syst 14(1):96–103

    Article  Google Scholar 

  31. Myerson J, Green L, Warusawitharana M (2001) Area under the curve as a measure of discounting. J Exper Anal Behav 76(2):235–243

    Article  Google Scholar 

  32. Naphade M, Huang T (2002) Extracting semantics from audiovisual content: the final frontier in multimedia retrieval. IEEE Trans Neural Netw 13(4):793–810

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Papadakis P, Pratikakis I, Theoharis T, Perantonis S (2010) Panorama: a 3d shape descriptor based on panoramic views for unsupervised 3d object retrieval. Int J Comput Vis 89(2–3):177–192

    Article  Google Scholar 

  35. Pass G, Zabih R (1996) Histogram refinement for content-based image retrieval, pp 96–102

  36. Pass G, Zabih R, Miller J (1996) Comparing images using color coherence vectors, pp 65–73

  37. Pencina M, D’Agostino Sr. R, D’Agostino Jr. R, Vasan R (2008) Evaluating the added predictive ability of a new marker: From area under the roc curve to reclassification and beyond. Stat Med 27(2):157–172

    Article  MathSciNet  Google Scholar 

  38. Perkins N, Schisterman E (2006) The inconsistency of ”optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol 163(7):670–675

    Article  Google Scholar 

  39. Provost F, Domingos P (2003) Tree induction for probability-based ranking. Mach Learn 52(3):199–215

    Article  MATH  Google Scholar 

  40. Shilane P, Min P, Kazhdan M, Funkhouser T (2004) The princeton shape benchmark. In: Shape modeling applications, 2004. Proceedings, pp 167–178. IEEE

  41. 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 

  42. Wang J, Bensmail H, Gao X (2012) Multiple graph regularized protein domain ranking. BMC Bioinforma 13:307. doi:10.1186/1471-2105-13-307

  43. Wang J, Bensmail H, Yao N, Gao X (2013) Discriminative sparse coding on multi-manifolds. Knowl-Based Syst 54:199–206

  44. Wang M, Gao Y, Lu K, Rui Y (2013) View-based discriminative probabilistic modeling for 3d object retrieval and recognition. IEEE Trans Image Process 22(4):1395–1407

    Article  MathSciNet  Google Scholar 

  45. Wang J, Bensmail H, Gao X (2014) Feature selection and multi-kernel learning for sparse representation on manifold. Neural Netw 51:9–16

    Google Scholar 

  46. Yanagawa A, Hsu W, Chang SF (2006) Brief descriptions of visual features for baseline trecvid concept detectors. Columbia University ADVENT Technical Report, pp 219–2006

  47. Yang Y, Nie F, Xu D, Luo J, Zhuang Y, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Anal Mach Intell 34(4):723–742

    Article  Google Scholar 

  48. Yang Y, Xu D, Nie F, Luo J, Zhuang Y (2009) Ranking with local regression and global alignment for cross media retrieval, pp 175–184

  49. Yang Y, Zhuang YT, Wu F, Pan YH (2008) Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval. IEEE Trans Multimedia 10(3):437–446

    Article  Google Scholar 

  50. Yue Y, Finley T, Radlinski F, Joachims T (2007) A support vector method for optimizing average precision. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, pp 271–278. ACM

  51. YuJie L, Feng B, ZongMin L, Hua L (2013) 3d model retrieval based on 3d fractional fourier transform. Int Arab J Inf Tech 10(5)

  52. Zhang D, Lu G (2003) Evaluation of mpeg-7 shape descriptors against other shape descriptors. Multimed Syst 9(1):15–30

    Article  Google Scholar 

  53. Zhao G, Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928

    Article  Google Scholar 

  54. Zhou D, Weston J, Gretton A, Bousquet O, Schölkopf B (2003) Ranking on data manifolds. Adv Neural Inf Process Syst 16:169–176

    Google Scholar 

  55. Zhu X, Huang Z, Cheng H, Cui J, Shen H (2013) Sparse hashing for fast multimedia search. ACM Trans Inf Syst 31(2)

  56. Zhu X, Huang Z, Yang Y, Tao Shen H, Xu C, Luo J (2013) Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recog 46(1):215–229

    Article  MATH  Google Scholar 

  57. Zhuang YT, Yang Y, Wu F (2008) Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval. IEEE Trans Multimed 10(2):221–229

    Article  Google Scholar 

Download references

Acknowledgements

Jim Jing-Yan Wang and Yijun Sun are in part supported by US National Science Foundation under grant No. DBI-1062362. The study is supported by grants from Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China, and King Abdullah University of Science and Technology (KAUST), Saudi Arabia.

Author information

Authors and Affiliations

  1. New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, The State University of New York, Buffalo, NY, 14203, USA

    Jim Jing-Yan Wang & Yijun Sun

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China

    Jim Jing-Yan Wang

  3. Department of Microbiology and Immunology, Department of Computer Science and Engineering, Department of Biostatistics, University at Buffalo, The State University of New York, Buffalo, NY, 14203, USA

    Xin Gao

Authors
  1. Jim Jing-Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yijun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J.JY., Sun, Y. & Gao, X. Sparse structure regularized ranking. Multimed Tools Appl 74, 635–654 (2015). https://doi.org/10.1007/s11042-014-1939-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-1939-9

Keywords

Search

Navigation