Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Evaluating classifier combination in object classification

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Classifier combination is used in object classification to combine the strength of multiple complementary classifiers and yield better performance than any single classifier. While various optimization-based combination methods have been presented in literature, their real effectiveness in practice has been called in question. This prompts us to investigate the behavior of classifiers in combination with the simple average combination method. Specifically, we investigate the influence of some issues on average classifier combination performance with extensive experiments on four diverse datasets. As a result, we find that the behavior of features and kernel functions in feature combination, and of soft labels and classifiers in classifier fusion, can be elegantly explained in the framework of the kNN method in instance-based learning. This framework shows that by proper selection of features, kernel functions, soft labels and classifiers, an enhanced average combination is able to perform much better than the average combination of all features, kernel functions, soft labels and classifiers. Furthermore, this framework gives rise to the descending combination performance curve (DCPC) as a new performance evaluation criterion of combination methods. Unlike the ordinary criterion of comparing only the final classification rate, DCPC also captures the ability of combination methods to combine the strength and avoid the drawbacks of multiple classifiers.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Altmann A, Rosen-Zve M, Prosperi M, Aharoni E, Neuvirth H et al (2008) Comparison of classifier fusion methods for predicting response to anti hiv-1 therapy. PLoS One 3(10):1–9

    Article  Google Scholar 

  2. Barla A, Odone F, Verri A (2003) Histogram intersection kernel for image classification. In: International conference on image processing, pp 513–516

  3. Bay H, Ess A, Tuytelaars T, Gool LV (2008) Surf: speeded up robust features. Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  4. Bickel S, Scheffer T (2004) Multi-view clustering. In: International conference on data mining, pp 19–26

  5. Bosch A, Zisserman A, Munoz X (2007) Representing shape with a spatial pyramid kernel. In: ACM international conference on image and video retrieval, pp 401–408

  6. Chibelushi CC, Deravi F, Mason JSD (1999) Adaptive classifier integration for robust pattern recognition. IEEE Trans Syst Man Cybern Part B Cybern 29(6):902–907

    Article  Google Scholar 

  7. Dalal N, Triggs B (2005) Histogram of oriented graidents for human detection. IEEE Int Conf Comput Vis Pattern Recogn 1:886–893

    Google Scholar 

  8. Duin RPW, Juszczak P, Paclik P, Pekalska E, Ridder D, Tax DMJ, Verzakov S (2007) Prtools4.1, a matlab toolbox for pattern recognition. In: Technical report, Delft University of Technology

  9. Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: CVPR workshop on generative-model based vision, p 178

  10. Gehler P, Nowozin S (2009) On feature combination for multiclass object classification. In: IEEE international conference on computer vision, pp 221–228

  11. Gonen M, Alpaydin E (2008) Localized multiple kernel learning. In: International conference on machine learning, pp 642–651

  12. Heerden CV, Barnard E (2009) Combining multiple classifiers for age classification. In: 20th annual symposium of the Pattern Recognition Association of South Africa, pp 59–64

  13. Ho TK, Hull JJ, Srihari SN (1994) Decision combination in multiple classifier systems. IEEE Trans Pattern Anal Mach Intell 16(1):66–75

    Article  Google Scholar 

  14. Hou J, Pelillo M (2013) A simple feature combination method based on dominant sets. Pattern Recogn Lett 46(11):3129–3139

    Article  Google Scholar 

  15. Hou J, Zhang BP, Qi NM, Yang Y (2011) Evaluating feature combination in object classification. In: International symposium on visual computing, pp 597–606

  16. Jia LL, Fei-Fei L (2007) What, where and who? Classifying event by scene and object recognition. In: IEEE international conference on computer vision, pp 1–8

  17. Kim HC, Ghahramani Z (2012) Bayesian classifier combination. In: International conference on artificial intelligence and statistics, pp 619–627

  18. Kloft M, Brefeld U, Sonnenburg S, Zien A (2011) Lp-norm multiple kernel learning. J Mach Learn Res 12:953–977

    MATH  MathSciNet  Google Scholar 

  19. Kumar A, Sminchisescu C (2007) Support kernel machines for object recognition. In: IEEE international conference on computer vision, pp 1–8

  20. Kuncheva LI (2002) A theoretical study on six classifier fusion strategies. IEEE Trans Pattern Anal Mach Intell 24(2):281–286

    Article  Google Scholar 

  21. Kuncheva LI, Bezdek JC, Duin RPW (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition 34(2):299–314

    Article  MATH  Google Scholar 

  22. Lanckriet G, Cristianini N, Bartlett P, Ghaoui L, Jordan M (2004) Learning the kernel matrix with semidefinite programming. J Mach Learn Res 5:27–72

    MATH  Google Scholar 

  23. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Int Conf Comput Vis Pattern Recogn 2:2169–2178

    Google Scholar 

  24. Lee DS (1995) Theory of classifier combination: the neural network approach. Ph.D. thesis, SUNY at Buffalo

  25. Lin YY, Liu TL, Fuh CS (2007) Local ensemble kernel learning for object category recognition. In: IEEE international conference on computer vision and pattern recognition, pp 1–8

  26. Liu W, Tao D (2013) Multiview hessian regularization for image annotation. IEEE Trans Image Process 22(7):2676–2687

    Article  MathSciNet  Google Scholar 

  27. Liu W, Tao D, Cheng J, Tang Y (2014) Multiview hessian discriminative sparse coding for image annotation. Comput Vis Image Underst 118(1):50–60

    Article  Google Scholar 

  28. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  29. Ludwig O, Delgado D, Goncalves V, Nunes U (2009) Trainable classifier-fusion schemes: an application to pedestrian detection. In: International conference on intelligent transportation systems, pp 1–6

  30. Luo Y, Tao D, Geng B, Xu C, Maybank SJ (2013) Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans Image Process 22(2):523–536

    Article  MathSciNet  Google Scholar 

  31. Luo Y, Tao D, Xu C, Liu H, Wen Y (2013) Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans Neural Netw Learn Syst 24(5):709–722

    Article  Google Scholar 

  32. Matas J, Chum O, Urban M, Pajdla T (2002) Robust wide-baseline stereo from maximally stable extremal regions. Br Mach Vis Conf 1:384–393

    Google Scholar 

  33. Nilsback ME, Zisserman A (2006) A visual vocabulary for flower classification. In: IEEE international conference on computer vision, pp 1447–1454

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

    Article  Google Scholar 

  35. 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 

  36. Schuffler P, Fuchs T, Ong C, Roth V, Buhmann J (2010) Computational TMA analysis and cell nucleus classification of renal cell carcinoma. In: 32 annual symposium of the German Pattern Recognition Society, pp 202–211

  37. Shechtman E, Irani M (2007) Matching local self-similarities across imagesn and videos. In: IEEE international conference on computer vision and pattern recognition, pp 1–8

  38. Tao D, Jin L (2012) Discriminative information preservation for face recognition. Neurocomputing 91:11–20

    Article  Google Scholar 

  39. Tao D, Jin L, Liu W, Li X (2013) Hessian regularized support vector machines for mobile image annotation on the cloud. IEEE Trans Multimed 15(4):833–844

    Article  Google Scholar 

  40. Tulyakov S, Jaeger S, Govindaraju V, Doermann D (2008) Review of classifier combination methods. Springer, Berlin, Hiedelberg

  41. Ulas A, Duin R, Castellani U, Loog M, Bicego M, Murino V, Bellani M, Cerruti S, Tansella M, Brambilla P (2010) Dissimilarity-based detection of schizophrenia. In: ICPR workshop on pattern recognition challenges in FMRI neuroimaging, pp 32–35

  42. Varma M, Ray D (2007) Learning the discriminative power-invariance trade-off. In: IEEE international conference on computer vision, pp 1–8

  43. Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Image Vis Comput 62(1-2):61–81

    Article  Google Scholar 

  44. Veeramachaneni K, Yan W, Goebel K, Osadciw L (2007) Improving classifier fusion using particular swarm optimization. In: IEEE symposium on computational intelligence in multicriteria decision making, pp 128–135

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

    Article  Google Scholar 

  46. Xu L, Krzyzak A, Suen CY (1992) Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybern Part B 23(3):418–435

    Article  Google Scholar 

  47. Yang JJ, Li YN, Tian YH, Duan LY, Gao W (2009) Group-sensitive multiple kernel learning for object categorization. In: IEEE international conference on computer vision, pp 436–443

  48. Yu J, Liu D, Tao D, Seah HS (2012) On combining multiple features for cartoon character retrieval and clip synthesis. IEEE Trans Syst Man Cybern Part B 42(5):1413–1427

    Article  Google Scholar 

  49. Yu J, Tao D, Rui Y, Cheng J (2013) Pairwise constraints based multiview features fusion for scene classification. Pattern Recognition 46(2):483–496

    Article  MATH  Google Scholar 

  50. Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272

    Article  MathSciNet  Google Scholar 

  51. Yu J, Wang M, Tao D (2012) Semisupervised multiview distance metric learning for cartoon synthesis. IEEE Trans Image Process 21(11):4636–4648

    Article  MathSciNet  Google Scholar 

  52. Zhao Z, Liu H (2008) Multi-source feature selection via geometry dependent covariance analysis. J Mach Learn Res Proc Track 4:36–47

    Google Scholar 

  53. Zhou D, Burges C (2007) Spectral clustering and transductive learning with multiple views. In: International conference on machine learning, pp 1159–1166

  54. Zhou D, Huang J, Schlkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding. In: Advances in neural information processing systems, pp 1601–1608

  55. Zien A, Ong CS (2007) Multiclass multiple kernel learning. In: International conference on machine learning, pp 1191–1198

Download references

Acknowledgments

This work was supported by Scientific Research Fund of Liaoning Provincial Education Department under Grant No. L2012400 and National Natural Science Foundation of China under Contract No. 61171189.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Bohai University, Jinzhou, 121013, China

    Jian Hou & Xu E

  2. School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China

    Qi Xia & Nai-Ming Qi

Authors
  1. Jian Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu E
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nai-Ming Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian Hou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hou, J., E, X., Xia, Q. et al. Evaluating classifier combination in object classification. Pattern Anal Applic 18, 799–816 (2015). https://doi.org/10.1007/s10044-014-0366-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-014-0366-x

Keywords

Search

Navigation