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Utilising Fuzzy Rough Set Based on Mutual Information Decreasing Method for Feature Reduction in an Image Retrieval System

  • Maryam Shahabi LotfabadiEmail author
  • Mohd Fairuz Shiratuddin
  • Kok Wai Wong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 313)

Abstract

Content-Based Image Retrieval (CBIR) system has become a focus of research in the area of image processing and machine vision. General CBIR system automatically index and retrieve images with visual features such as colour, texture and shape. However, current research found that there is a significant gap between visual features and semantic features used by humans to describe images. In order to bridge the semantic gap, some researchers have proposed methods for managing and decreasing image features, and extract useful features from a feature vector. This paper presents an image retrieval system utilising fuzzy rough set based on mutual information decreasing method and the Support Vector Machine (SVM) classifier. The system has training and testing phases. In order to reduce the semantic gap, the propose retrieval system used relevance feedback to improve the retrieval performance. This paper also compared the proposed method with other traditional retrieval systems that use PCA, kernel PCA, Isomap and MVU for their feature reduction method. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the propose method can retrieve images more efficiently than the traditional methods. The use of fuzzy rough set based on mutual information decreasing method, SVM and relevance feedback ensures that the propose image retrieval system produces results which are highly relevant to the content of an image query.

Keywords

CBIR system Fuzzy rough set Mutual information Relevance feedback 

References

  1. 1.
    Cerra, D. and M. Datcu, A fast compression-based similarity measure with applications to content-based image retrieval. Journal of Visual Communication and Image Representation, 2012. 23(2): p. 293-302.Google Scholar
  2. 2.
    Wang, X.-y., Z.-f. Chen, and J.-j. Yun, An effective method for color image retrieval based on texture. Computer Standards & Interfaces, 2012. 34(1): p. 31-35.Google Scholar
  3. 3.
    Iqbal, K., M.O. Odetayo, and A. James, Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics. Journal of Computer and System Sciences, 2012. 78(4): p. 1258-1277.Google Scholar
  4. 4.
    Foithong, S., O. Pinngern, and B. Attachoo, Feature subset selection wrapper based on mutual information and rough sets. Expert Systems with Applications, 2012. 39(1): p. 574-584.Google Scholar
  5. 5.
    Penatti, O.v.A.B., E. Valle, and R.d.S. Torres, Comparative study of global color and texture descriptors for web image retrieval. Journal of Visual Communication and Image Representation, 2012. 23(2): p. 359-380.Google Scholar
  6. 6.
    Yildizer, E., et al., Integrating wavelets with clustering and indexing for effective content-based image retrieval. Knowledge-Based Systems, 2012. 31(0): p. 55-66.Google Scholar
  7. 7.
    Krishnamoorthi, R. and S. Sathiya devi, A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model. Journal of Visual Communication and Image Representation, 2012. 23(1): p. 18-30.Google Scholar
  8. 8.
    Chen, Z., et al., An Annotation Rule Extraction Algorithm for Image Retrieval. Pattern Recognition Letters, 2012. 33(10): p. 1257–1268.Google Scholar
  9. 9.
    Subrahmanyam, M., R.P. Maheshwari, and R. Balasubramanian, Expert system design using wavelet and color vocabulary trees for image retrieval. Expert Systems with Applications, 2012. 39(5): p. 5104-5114.Google Scholar
  10. 10.
    F.F.Xu, D.Q. Miao, and L. Wei., Fuzzy-Rough Attribute Reduction Via Mutual Information With An Application To Cancer Classification. Computers And Mathematics With Applications, 2009. 57: p. 1010_1017.Google Scholar
  11. 11.
    Hotelling, H., Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 1933. 24: p. 417-441.Google Scholar
  12. 12.
    Balasubramanian, M. and E.L. Schwartz, The Isomap algorithm and topological stability. . Science 2002. 295(5552): p. 7.Google Scholar
  13. 13.
    Hoffmann., H., Kernel PCA for novelty detection. Pattern Recognition, 2007. 40(3): p. 863-874.Google Scholar
  14. 14.
    K.Q. Weinberger, F. Sha, and L.K. Saul., Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the 21st International Conference on Machine Learning, 2004.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Maryam Shahabi Lotfabadi
    • 1
    Email author
  • Mohd Fairuz Shiratuddin
    • 1
  • Kok Wai Wong
    • 1
  1. 1.School of Information TechnologyMurdoch UniversityMurdochAustralia

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