Location of Repository

Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

By D. Tao, X. Tang, Xuelong Li and Y. Rui


In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms

Topics: csis
Publisher: IEEE Computer Society
Year: 2006
OAI identifier: oai:eprints.bbk.ac.uk.oai2:452

Suggested articles



  1. (2001). A direct LDA algorithm for high-dimensional data with application to face recognition,” doi
  2. (2004). A direct method to solve the biased discriminant analysis in kernel feature space for content-based image retrieval,” doi
  3. (1992). An efficient algorithm for Foley-Sammon optimal set of discriminant vectors by algebraic method,” doi
  4. (2001). An introduction to kernel-based learning algorithms,” doi
  5. (2001). Color and texture descriptors circuits and systems for video technology,” doi
  6. (1991). Color indexing,” doi
  7. (2001). Comparing discriminate transformations and SVM for learning during multimedia retrieval,” in doi
  8. (1996). Comparing images using color coherence vectors,” in doi
  9. (1998). Content-based image indexing and retrieval,” doi
  10. (2000). Contentbased image retrieval at the end of the early years,” doi
  11. (1996). Discriminant analysis and eigenspace partition tree for face and object recognition from views,” in doi
  12. (1998). Discriminant analysis of principal components for face recognition,” in doi
  13. (2003). Face recognition using kernel direct discriminant analysis algorithms,” doi
  14. (2001). Image indexing with mixture hierarchies,” in doi
  15. (1996). Image retrieval using color and shape,” doi
  16. (1999). Image retrieval: current techniques, promising directions and open issues,” doi
  17. (2000). Incorporate support vector machines to content-based image retrieval with relevant feedback,” in doi
  18. (2000). Incorporate support vector machines tocontent-basedimageretrievalwithrelevantfeedback,”inProc.ICIP,
  19. (2002). Incremental singular value decomposition of uncertain data with missing values,” in doi
  20. (1992). J.MaoandA.Jain,“Textureclassificationandsegmentationusingmultiresolution simultaneous autoregressive models,”
  21. (2002). Learning similarity measure for natural image retrieval with relevance feedback,” doi
  22. (2002). M.Brand,“Incrementalsingularvaluedecompositionofuncertaindata with missing values,” in
  23. (1990). Matrix Analysis. doi
  24. (1998). Mindreader: querying databases through multiple examples,” in
  25. (2001). One-class SVM for learning in image retrieval,” in doi
  26. (1992). Optimal fisher discriminant analysis using the rank decomposition,” doi
  27. (1998). Relevance feedback techniques in interactive content-based image retrieval,” in doi
  28. (1998). Relevance feedback: a power tool in interactive content-based image retrieval,” doi
  29. (1998). Shape-based retrieval: a case study with trademark image databases,” doi
  30. (2001). SIMPLIcity: semantics-sensitive integrated matching for picture libraries,” doi
  31. (2001). Small sample learning during multimedia retrieval using biasmap,” in doi
  32. (1990). Statistical Pattern Recognition, 2nd ed. doi
  33. (2001). Support vector machine learning for image retrieval,” in doi
  34. (1993). Texture analysis and classification with treestructured wavelet transform,” doi
  35. (1992). Texture classification and segmentation using multiresolution simultaneous autoregressive models,” doi
  36. (1978). Texture features corresponding to visual perception,” doi
  37. (1996). Texture features for browsing and retrieval of image data,” doi
  38. (1995). The Nature of Statistical Learning Theory. doi
  39. (1993). The QBIC project: querying images by content using color, texture, and shape,” in doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.