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

Learning and intelligence can happen everywhere, a case study: learning via Non-uniform 1D rulers with applications in image classification and recognition

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

Abstract

In this paper, we presented a non-uniform 1D ruler model and applied it in various image classification and image recognition scenarios, and some are for military technology usage. Our model is very simple, elegant and original, which is solved by convex quadratic programming. It has wide applications in pattern recognition and intelligent multimedia data analysis. We believe that a new research topic, namely, numeric calibration, has started, which is parallel to dimensionality reduction, feature selection, or metric learning etc. Our methods can be used as a pre-processing step for metric learning methods, in which, our learned calibrated feature space is used as input for them. The various combinations of our methods and metric learning methods, may lead to new interesting research problems.

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
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. Binning can be applied, if the feature data numerics are continuous, or the numerics rarely repeat in the dataset, but the performance may be suboptimal. Here binning refers to finding appropriate split points to convert continuous numerics into a number of discrete bins. See [11] for a survey and performance evaluation among several popular binning methods.

  2. Sometimes it is called label distance, or ideal distance.

References

  1. Cabral R, De la Torre F, Costeira JP, Bernardino A (2015) Matrix completion for weakly-supervised multilabel image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(1):121–135

    Article  Google Scholar 

  2. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(27):1–27

    Article  Google Scholar 

  3. Chen G, Song Y, Wang F, Zhang C (2008) Semi-supervised Multi-label Learning by Solving a Sylvester Equation. SIAM Conference on Data Mining: 410–419

  4. Fan N (2011) Learning nonlinear distance functions using neural network for regression with application to robust human age estimation. ICCV:249–254

  5. Gao S, Tsang I, Chia L, Zhao P (2010) Local features are not lonely – Laplacian sparse coding for image classification. CVPR:1–7

  6. Guan YP, Huang YZ (2015) Multi-pose human head detection and tracking boosted by efficient human head validation using ellipse detection. Eng Appl Artif Intell 37:181–193

    Article  Google Scholar 

  7. Huang Y, Long Y (2006) Super-resolution using neural networks based on the optimal recovery theory. J Comput Electron 5(4):275–281

    Article  Google Scholar 

  8. Ji S, Tang L, Yu S, Ye J (2008) Extracting shared subspace for multi-label classification. SIGKDD: 381–389

  9. Li L, Li F (2007) What, where and who? Classifying events by scene and object recognition. ICCV:1–8

  10. Long Y, Huang Y (2006) Image based source camera identification using demosaicking. Proceedings of IEEE 8th Workshop on Multimedia Signal Processing, Victoria, Canada, pp. 419–424.

  11. Macskassy SA, Hirsh H, Banerjee A, Dayanik AA (2003) Converting numerical classification into text classification. Artif Intell 143(1):51–77

    Article  MathSciNet  MATH  Google Scholar 

  12. Naphade M, Kennedy L, Kender J, Chang S, Smith J, Over P, Hauptmann A (2005) LSCOM-lite: A light scale concept ontology for multimedia understanding for TRECVID 2005. IBM Research Tech Report RC23612(W0505-104)

  13. Russell B, Torralba A, Murphy K, Freeman W (2008) LabelMe: a database and web-based tool for image annotation. Int J Comput Vis 77(1):157–173

    Article  Google Scholar 

  14. Schiffman SS, Reynolds ML, Young FW (1981) Introduction to multidimensional scaling. Academic Press, NY

    MATH  Google Scholar 

  15. Sturm JF (1999) Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones. Optimization Methods and Software 11:625–653

    Article  MathSciNet  MATH  Google Scholar 

  16. Sun FM, Tang JH, Li HJ, Qi GJ, Huang TS (2014) Multi-label image categorization with sparse factor representation. IEEE Trans Image Process 23(3):1028–1037

    Article  MathSciNet  Google Scholar 

  17. Wang C, Blei D, Li F (2009a) Simultaneous image classification and annotation, CVPR, pp. 1903--1910

  18. Wang H, Huang H, Ding C (2009b) Image annotation using multi-label correlated green’s function. ICCV, pp: 2029–2034

  19. Weinberger K, Blitzer J, Saul L (2006) Distance metric learning for large margin nearest neighbor classification. NIPS: 1475–1482

  20. Xiao B, Yang X, Xu Y, Zha H (2009) Learning distance metric for regression by semidefinite programming with application to human age estimation. ACM MM:451–460

  21. Yang L, Jin R (2006) Distance metric learning: a comprehensive survey, Technical report, Michigan State University. http://www.cs.cmu.edu/~liuy/frame_survey_v2.pdf

  22. Yu K, Yu SP, Tresp V (2005) Multi-label informed latent semantic indexing. SIGIR: 258–265

  23. Zha ZJ, Mei T, Wang J, Wang Z, Hua XS (2009) Graph-based semi-supervised learning with multiple labels. J Vis Commun Image Represent 20(2):97–103

    Article  Google Scholar 

  24. Zhao GY, Ahonen T, Matas J, Pietikainen M (2012) Rotation-invariant image and video description with local binary pattern features. IEEE Trans Image Process 21(4):1465–1477

    Article  MathSciNet  Google Scholar 

  25. Zhou DY, Bousquet O, Lal TN,Weston J, Scholkopf B (2004) Learning with local and global consistency, NIPS

  26. Zhu XJ, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using Gaussian fields and harmonic functions. ICML: 912–919

Download references

Acknowledgments

This research work is funded by Natural Science Foundation of China (Grant No.11176016, 60872117), and Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20123108110014).

Author information

Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Yizhen Huang & Yepeng Guan

  2. Key Laboratory of Advanced Displays and System Application, Ministry of Education, Shanghai, China

    Yepeng Guan

Authors
  1. Yizhen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yepeng Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yepeng Guan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Y., Guan, Y. Learning and intelligence can happen everywhere, a case study: learning via Non-uniform 1D rulers with applications in image classification and recognition. Multimed Tools Appl 76, 913–929 (2017). https://doi.org/10.1007/s11042-015-3043-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3043-1

Keywords

Search

Navigation