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Multitraining support vector machine for image retrieval

By J. Li, N. Allinson, D. Tao and Xuelong Li

Abstract

Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively

Topics: csis
Publisher: IEEE Signal Processing Society
Year: 2006
OAI identifier: oai:eprints.bbk.ac.uk.oai2:453

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Citations

  1. (1998). A tutorial on support vector machines for pattern recognition,” doi
  2. (1998). A tutorial on support vector machines for pattern recognition,”DataMiningKnowl.Discovery,vol.2,no.2,pp.121–167,Feb.
  3. (2001). Color and texture descriptors,” doi
  4. (1991). Color indexing,” doi
  5. Combining labeled and unlabeled data with cotraining,” COLT: doi
  6. (2000). Content-based image retrieval at the end of the early years,” doi
  7. (1997). Content-based image retrieval with relevance feedback in MARS,” in doi
  8. (2004). Content-based image retrieval: An overview,” in Emerging Topics in Computer Vision. doi
  9. (2006). Direct kernel biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm,” doi
  10. (2006). Direct kernel biased discriminant analysis:Anewcontent-basedimageretrievalrelevancefeedbackalgorithm,” doi
  11. (2004). Dynamic learning from multiple examples for semantic object segmentation and search,” doi
  12. (2000). Incorporate support vector machines to content-based image retrieval with relevant feedback,” doi
  13. Incorporate support vector machinestocontent-basedimageretrievalwithrelevantfeedback,”inPro.
  14. (2002). Learning similarity measure for natural image retrieval with relevance feedback,” doi
  15. (2003). Object segmentation and labeling by learning from examples,” doi
  16. (1998). On combining classifiers,” doi
  17. (2001). One-class SVM for learning in image retrieval,” in doi
  18. (1999). OSU SVM classifier matlab toolbox (ver. 3.00),” in Pulsed Neural Networks.
  19. (2001). Small sample learning during multimedia retrieval using biasmap,” in doi
  20. (2001). Small sample learning during multimedia retrievalusingbiasmap,”in Proc.IEEEInt.Conf.ComputerVisionand Pattern Recognition,
  21. (2001). Support vector machine learning for image retrieval,” in doi
  22. (1995). The Nature of Statistical Learning Theory. doi
  23. (1998). The random subspace method for constructing decision forests,” doi

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