An application of one-class support vector machines in content-based image retrieval

Expert Systems with Applications

Volumn 33, Issue 2, 2007, Pages 491-498

  Seo, Kwang Kyu ^a  

^a Sangmyung University   (South Korea)

Author keywords

Artificial neural networks; Content based image retrieval; One class classification; Support vector machines

Indexed keywords

DATABASE SYSTEMS; IMAGE PROCESSING; LEARNING SYSTEMS; NEURAL NETWORKS;

APPLICATION AREA; MULTILAYER PERCEPTION; ONE CLASS CLASSIFICATION; SUPPORT VECTOR MACHINES;

CONTENT BASED RETRIEVAL;

EID: 33846420617     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2006.05.030     Document Type: Article

References (22)

1. Cooper D.R., and Emory C.W. Business research methods (1995), Irwin, Chicago, IL
2. Dumais, S., Platt, J., Heckerman, D., & Sahami, M. (1998). Inductive learning algorithms and representations for text categorization. In Proceedings of ACM-CIKM98, Washington, DC (pp. 148-155).
3. Fournier, J., Cord, M., & Philipp-Foliguet, S. (2001). Back-propagation algorithm for relevance feedback in image retrieval. In IEEE international conference in image processing (ICIP'01) (Vol. 1, pp. 686-689).
4. Haralick R.M., Shanmugam K., and Dinstein I. Texture features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3 6 (1973) 610-621
5. Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2004). A practical guide to support vector classification. Technical report. Department of Computer Science and Information Engineering, National Taiwan University.
6. Jain A.K., and Vailaya A. Image retrieval using color and shape. Pattern Recognition 29 (1996) 1233-1244
7. Joachims, T. (1997). Text categorization with support vector machines. Technical report, LS VIII Number 23, University of Dormund.
8. Koskela M., Laaksonen J., and Oja E. Use of image subset features in image retrieval with self-organizing maps. LNCS 3115 (2004) 508-516
9. Min J.H., and Lee Y.-C. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications 28 4 (2005) 603-614
10. Müller K.R., Mika S., Ratsch G., Tsuda K., and Schölkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12 2 (2001) 181-201
11. Osuna E., Freund R., and Girosi F. Training support vector machines: An application to face detection. 1997 Conference on computer vision and pattern recognition (1997), IEEE, Puerto Rico 130-136
12. Pakkanen J., Iivarinen J., and Oja E. The evolving tree - A novel self-organizing network for data analysis. Neural Processing Letters 20 3 (2004) 199-211
13. Park S.-S., Seo K.-K., and Jang D.-S. Expert system based on artificial neural networks for content-based image retrieval. Expert Systems with Applications 29 3 (2005) 589-597
14. Roobaert D., and Hulle V.M. View-based 3d-object recognition with support vector machines. 1999 IEEE workshop on neural networks for signal processing (1999), IEEE, Madison, WI 77-84
15. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. Parallel distribution processing Vol. 1 (1986), Cambridge, MA, MIT Press
16. Schölkopf B., and Smola A. Learning with kernels - support vector machines, regularization, optimization and beyond (2002), MIT Press, Cambridge, MA
17. Shin K.-S., Lee T.S., and Kim H.J. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications 28 1 (2005) 127-135
18. Smeulders A.W.M., Worring M., Santini S., Gupta A., and Jain R. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 12 (2000) 1349-1380
19. Su Z., Zhang H., Li S., and Ma S. Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Transactions on Image Processing 12 8 (2003) 924-937
20. Sun Z., Bebis G., and Miller R. Object detection using feature subset selection. Pattern Recognition 27 (2004) 2165-2176
21. Vapnik V. The nature of statistical learning theory (1995), Springer, Berlin
22. Vapnik V. Statistical learning theory (1998), Springer, New York