Multimedia Tools and Applications

, Volume 74, Issue 2, pp 635–654 | Cite as

Sparse structure regularized ranking



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.


Multimedia database retrieval Ranking score Sparse representation 



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.


  1. 1.
    Agichtein E, Brill E, Dumais S (2006) Improving web search ranking by incorporating user behavior information, pp 19–26Google Scholar
  2. 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):2037–2041CrossRefGoogle Scholar
  3. 3.
    Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Bian W, Tao D (2010) Biased discriminant euclidean embedding for content-based image retrieval. IEEE Trans Image Process 19(2):545–554CrossRefMathSciNetGoogle Scholar
  5. 5.
    Bober M (2001) Mpeg-7 visual shape descriptors. IEEE Trans Circ Syst Video Tech 11(6):716–719CrossRefGoogle Scholar
  6. 6.
    Breu H, Gil J, Kirkpatrick D, Werman M (1995) Linear time euclidean distance transform algorithms. IEEE Trans Pattern Anal Mach Intell 17(5):529–533CrossRefGoogle Scholar
  7. 7.
    Clausi D, Ed Jernigan M (2000) Designing gabor filters for optimal texture separability. Pattern Recog 33(11):1835–1849CrossRefGoogle Scholar
  8. 8.
    Cook N (2007) Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115(7):928–935CrossRefGoogle Scholar
  9. 9.
    Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves, pp 233–240Google Scholar
  10. 10.
    De Maesschalck R, Jouan-Rimbaud D, Massart D (2000) The mahalanobis distance. Chemom Intell Lab Syst 50(1):1–18CrossRefGoogle Scholar
  11. 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 536–547Google Scholar
  12. 12.
    Euzenat J (2007) Semantic precision and recall for ontology alignment evaluation, pp 348–353Google Scholar
  13. 13.
    Feng DD, Siu WC, Zhang HJ (2003) Multimedia information retrieval and management: technological fundamentals and applications. SpringerGoogle Scholar
  14. 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):354–366CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Gao X, Xiao B, Tao D, Li X (2008) Image categorization: graph edit distance + edge direction histogram. Pattern Recog 41(10):3179–3191CrossRefMATHGoogle Scholar
  16. 16.
    Grigorescu S, Petkov N, Kruizinga P (2002) Comparison of texture features based on gabor filters. IEEE Trans Image Process 11(10):1160–1167CrossRefMathSciNetGoogle Scholar
  17. 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):171–186CrossRefMATHGoogle Scholar
  18. 18.
    Hanley J, McNeil B (1982) The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1):29–36CrossRefGoogle Scholar
  19. 19.
    Haveliwala T (2003) Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans Knowl Data Eng 15(4):784–796CrossRefGoogle Scholar
  20. 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 1–1Google Scholar
  21. 21.
    Hiremath P, Pujari J (2007) Content based image retrieval using color, texture and shape features, pp 780–784Google Scholar
  22. 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 411–426Google Scholar
  23. 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):1665–1674CrossRefGoogle Scholar
  24. 24.
    Jain A, Farrokhnia F (1991) Unsupervised texture segmentation using gabor filters. Pattern Recog 24(12):1167–1186CrossRefGoogle Scholar
  25. 25.
    Kapela R, Rybarczyk A (2007) Real-time shape description system based on mpeg-7 descriptors. J Syst Archit 53(9):602–618CrossRefGoogle Scholar
  26. 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):6289–6300Google Scholar
  27. 27.
    Kim YW, Oh IS (2004) Watermarking text document images using edge direction histograms. Pattern Recog Lett 25(11):1243–1251CrossRefGoogle Scholar
  28. 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):1021–1030CrossRefGoogle Scholar
  29. 29.
    Mangai M, Gounden N (2013) Subspace-based clustering and retrieval of 3-d objects. Comput Electr Eng 39(3):809–817CrossRefGoogle Scholar
  30. 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):96–103CrossRefGoogle Scholar
  31. 31.
    Myerson J, Green L, Warusawitharana M (2001) Area under the curve as a measure of discounting. J Exper Anal Behav 76(2):235–243CrossRefGoogle Scholar
  32. 32.
    Naphade M, Huang T (2002) Extracting semantics from audiovisual content: the final frontier in multimedia retrieval. IEEE Trans Neural Netw 13(4):793–810CrossRefGoogle Scholar
  33. 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):971–987CrossRefGoogle Scholar
  34. 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):177–192CrossRefGoogle Scholar
  35. 35.
    Pass G, Zabih R (1996) Histogram refinement for content-based image retrieval, pp 96–102Google Scholar
  36. 36.
    Pass G, Zabih R, Miller J (1996) Comparing images using color coherence vectors, pp 65–73Google Scholar
  37. 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):157–172CrossRefMathSciNetGoogle Scholar
  38. 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):670–675CrossRefGoogle Scholar
  39. 39.
    Provost F, Domingos P (2003) Tree induction for probability-based ranking. Mach Learn 52(3):199–215CrossRefMATHGoogle Scholar
  40. 40.
    Shilane P, Min P, Kazhdan M, Funkhouser T (2004) The princeton shape benchmark. In: Shape modeling applications, 2004. Proceedings, pp 167–178. IEEEGoogle Scholar
  41. 41.
    Wang JZ, Li J, Wiederhold G (2001) Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963CrossRefGoogle Scholar
  42. 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. 43.
    Wang J, Bensmail H, Yao N, Gao X (2013) Discriminative sparse coding on multi-manifolds. Knowl-Based Syst 54:199–206Google Scholar
  44. 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):1395–1407CrossRefMathSciNetGoogle Scholar
  45. 45.
    Wang J, Bensmail H, Gao X (2014) Feature selection and multi-kernel learning for sparse representation on manifold. Neural Netw 51:9–16Google Scholar
  46. 46.
    Yanagawa A, Hsu W, Chang SF (2006) Brief descriptions of visual features for baseline trecvid concept detectors. Columbia University ADVENT Technical Report, pp 219–2006Google Scholar
  47. 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):723–742CrossRefGoogle Scholar
  48. 48.
    Yang Y, Xu D, Nie F, Luo J, Zhuang Y (2009) Ranking with local regression and global alignment for cross media retrieval, pp 175–184Google Scholar
  49. 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):437–446CrossRefGoogle Scholar
  50. 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. ACMGoogle Scholar
  51. 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)Google Scholar
  52. 52.
    Zhang D, Lu G (2003) Evaluation of mpeg-7 shape descriptors against other shape descriptors. Multimed Syst 9(1):15–30CrossRefGoogle Scholar
  53. 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):915–928CrossRefGoogle Scholar
  54. 54.
    Zhou D, Weston J, Gretton A, Bousquet O, Schölkopf B (2003) Ranking on data manifolds. Adv Neural Inf Process Syst 16:169–176Google Scholar
  55. 55.
    Zhu X, Huang Z, Cheng H, Cui J, Shen H (2013) Sparse hashing for fast multimedia search. ACM Trans Inf Syst 31(2)Google Scholar
  56. 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):215–229CrossRefMATHGoogle Scholar
  57. 57.
    Zhuang YT, Yang Y, Wu F (2008) Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval. IEEE Trans Multimed 10(2):221–229CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.New York State Center of Excellence in Bioinformatics and Life SciencesUniversity at Buffalo, The State University of New YorkBuffaloUSA
  2. 2.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina
  3. 3.Department of Microbiology and Immunology, Department of Computer Science and Engineering, Department of BiostatisticsUniversity at Buffalo, The State University of New YorkBuffaloUSA

Personalised recommendations