Color Image Retrieval Based on Interactive Genetic Algorithm

  • Chih-Chin Lai
  • Ying-Chuan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5579)


In order to efficiently and effectively retrieval the desired images from a large image database, the development of a user-friendly image retrieval system has been an important research for several decades. In this paper, we propose a content-based image retrieval method based on an interactive genetic algorithm (IGA). The mean value and the standard deviation of a color image are used as color features. In addition, we also considered the entropy based on the gray level co-occurrence matrix as the texture feature. Further, to bridge the gap between the retrieving results and the users’ expectation, the IGA is employed such that the users can adjust the weight for each image according to their expectations. Experimental results are provided to illustrate the feasibility of the proposed approach.


Content-based image retrieval interactive genetic algorithm 


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  1. 1.
    Antani, S., Kasturi, R., Jain, R.: A Survey of The Use of Pattern Recognition Methods for Abstraction, Indexing and Retrieval. Pattern Recognition 1, 945–965 (2002)CrossRefzbMATHGoogle Scholar
  2. 2.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Ltd., Chichester (2001)zbMATHGoogle Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  4. 4.
    Holland, J.: Adaptation in Natural and Artificial System. The University of Michigan Press, MI (1975)Google Scholar
  5. 5.
    Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. I. Addison Wesley, Reading (1992)Google Scholar
  6. 6.
    Lu, T.-C., Chang, C.-C.: Color Image Retrieval Technique Based on Color Features and Image Bitmap. Information Processing and Management 43, 461–472 (2007)CrossRefGoogle Scholar
  7. 7.
    Luo, X., Shishibori, M., Ren, F., Kita, K.: Incorporate Feature Space Transformation to Content-Based Image Retrieval with Relevance Feedback. International Journal of Innovative Computing, Information and Control 3, 1237–1250 (2007)Google Scholar
  8. 8.
    Liu, Y., Zhang, D., Lu, G., Ma, W.-Y.: A Survey of Content-Based Image Retrieval with High-Level Semantics. Pattern Recognition 40, 262–282 (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Sudhamani, M.V., Venugopal, C.R.: Multidimensional Indexing Structures for Content-Based Image Retrieval: A Survey. International Journal of Innovative Computing, Information and Control 4, 867–881 (2008)Google Scholar
  10. 10.
    Takagi, H.: Interactive Evolutionary Computation: Cooperation of Computational Intelligence and Human Kansei. In: 5th International Conference on Soft Computing, pp. 41–50. World Scientific Press, Fukuoka (1998)Google Scholar
  11. 11.
    Veitkamp, R.C., Tanase, M.: Content-Based Image Retrieval Systems: A Survey. Technical report, UU-CS-2000-34, University of Utrecht (2000)Google Scholar
  12. 12.
    Zhou, X.S., Huang, T.S.: Relevance Feedback in Content-Based Image Retrieval: Some Recent Advances. Information Science 48, 124–137 (2002)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Zheng, W.-M., Lu, Z.-M., Burkhardt, H.: Color Image Retrieval Schemes Using Index Histograms Based on Various Spatial-Domain Vector Quantizers. International Journal of Innovative Computing, Information and Control 2, 1317–1326 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chih-Chin Lai
    • 1
  • Ying-Chuan Chen
    • 2
  1. 1.Department of Electrical EngineeringNational University of KaohsiungKaohsiungTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational University of TainanTainanTaiwan

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