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Content based Image Retrieval using Implicit and Explicit Feedback with Interactive Genetic Algorithm

by Ghanshyam Raghuwanshi, Nishchol Mishra, Sanjeev Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 16
Year of Publication: 2012
Authors: Ghanshyam Raghuwanshi, Nishchol Mishra, Sanjeev Sharma
10.5120/6186-8665

Ghanshyam Raghuwanshi, Nishchol Mishra, Sanjeev Sharma . Content based Image Retrieval using Implicit and Explicit Feedback with Interactive Genetic Algorithm. International Journal of Computer Applications. 43, 16 ( April 2012), 8-14. DOI=10.5120/6186-8665

@article{ 10.5120/6186-8665,
author = { Ghanshyam Raghuwanshi, Nishchol Mishra, Sanjeev Sharma },
title = { Content based Image Retrieval using Implicit and Explicit Feedback with Interactive Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 16 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number16/6186-8665/ },
doi = { 10.5120/6186-8665 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:33:34.252535+05:30
%A Ghanshyam Raghuwanshi
%A Nishchol Mishra
%A Sanjeev Sharma
%T Content based Image Retrieval using Implicit and Explicit Feedback with Interactive Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 16
%P 8-14
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There are various new applications of genetic algorithms to information retrieval, mostly with respect to relevance feedback. However, they are yet to be evaluated in a way that allows them to be compared with each other and with other relevance feedback techniques. There is always need to efficiently store and retrieve image data to perform assigned tasks and to make a decision. This paper presents a new image retrieval framework with two types of relevance feedback i. e. , implicit feedback in combination with explicit feedback. This paper employs Interactive Genetic Approach to discover a combination of descriptors that better characterizes the user perception of image resemblance. This approach provides better management and retrieval of images than the keyword-based approach. However, most of the conventional methods do not have the capability to effectively incorporate human interaction and emotion into retrieving images. In order to solve this problem we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). In this approach we used two tier architecture of implicit and explicit feedback with IGA. Therefore, this system facilitates the search for the image not only with explicit queries, but also with implicit queries.

References
  1. M. Antonelli, S. G. Dellepiane, and M. Goccia, "Design and implementation of Web-based systems for image segmentation and CBIR," IEEETrans. Instrum. Meas. , vol. 55, no. 6, pp. 1869–1877, Dec. 2006 .
  2. N. Jhanwar, S. Chaudhuri, G. Seetharaman, and B. Zavidovique, "Content based image retrieval using motif co occurrence matrix," Image Vis. Comput. , vol. 22, no. 14, pp. 1211–1220, Dec. 2004.
  3. W. H. Hsu, L. S. Kennedy and S. -F. Chang. Reranking Methods for Visual Search. IEEE Multimedia, vol. 14, no. 3, pp. 14-22, 2007.
  4. Y. Jing and S. Baluja. Visualrank: Applying pagerank to large-scale image search. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, pp. 1877–1890, 2008.
  5. T. Yao, T. Mei and C. -W. Ngo. Co-reranking by mutual reinforcement for image search. CIVR, pp. 34-41, 2010.
  6. X. Tian, L. Yang, J. Wang, Y. Yang, X. Wu and X. -S. Hua. Bayesian Video Search Reranking. ACM Int'l Conf. Multimedia, pp. 131–140, 2008.
  7. ] J. Han, K. N. Ngan, M. Li, and H. -J. Zhang, "A memory learning framework for effective image retrieval," IEEE Trans. Image Process. , vol. 14, no. 4, pp. 511–524, Apr. 2005.
  8. ] H. Chen, "Machine learning for information retrieval: neural networks,symbolic learning, and genetic algorithms". Journal of the American Society for Information Science, 46(3), 1995, pp. 194–216.
  9. J. Savoy and D. Vrajitoru, "Evaluation of learning schemes used in information retrieval (CR-I-95-02)". Universite de Neuchatel, Facultededroitet des Sciences Economiques, 1996.
  10. G. Beligiannis, L. Skarlas, and S. Likothanassis, "A generic applied evolutionaryhybrid technique for adaptive system modeling and information mining," IEEE Signal Process. Mag. —Special Issue on "Signal Processing for Mining Information", vol. 21, no. 3, pp. 28–38, May 2004.
  11. G. N. Beligiannis, L. V. Skarlas, S. D. Likothanassis, and K. G. Perdikouri, "Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique," IEEE Trans. Instrum. Meas. , vol. 54, no. 6, pp. 2184–2190, Nov. 2005.
  12. C. -Y. Chang and D. -R. Chen, "Active noise cancellation without secondarypath identification by using an adaptive genetic algorithm," IEEE Trans. Instrum. Meas. , vol. 59, no. 9, pp. 2315–2327, Sep. 2010.
  13. S. Osowski, R. Siroic, T. Markiewicz, and K. Siwek, "Application of support vector machine and genetic algorithm for improved blood cell recognition," IEEE Trans. Instrum. Meas. , vol. 58, no. 7, pp. 2159–2168, Jul. 2009.
  14. C. Koutsojannis, G. Beligiannis, I. Hatzilygeroudis, C. Papavlasopoulos, and J. Prentzas, "Using a hybrid AI approach for exercise difficulty level adaptation," Int. J. Continuing Eng. Educ. Life-Long Learn. , vol. 17, no. 4/5, pp. 256–272, 2007.
  15. G. Beligiannis, I. Hatzilygeroudis, C. Koutsojannis, and J. Prentzas, "A GA driven intelligent system for medical diagnosis," in Proc. KES, vol. 4251. Heidelberg, Germany: Springer-Verlag, 2006, pp. 968–975.
  16. C. -H. Wu, H. -J. Chou, and W. -H. Su, "A genetic approach for coordinate transformation test of GPS positioning," IEEE Geosci. Remote Sens. Lett. , vol. 4, no. 2, pp. 297–301, Apr. 2007.
  17. S. -T. Pan, "Design of robust D-stable IIR filters using genetic algorithms with embedded stability criterion," IEEE Trans. Signal Process. , vol. 57, no. 8, pp. 3008–3016, Aug. 2009.
  18. G. Paravati, A. Sanna, B. Pralio, and F. Lamberti, "A genetic algorithm for target tracking in FLIR video sequences using intensity variation function," IEEE Trans. Instrum. Meas. , vol. 58, no. 10, pp. 3457–3467, Oct. 2009.
  19. S. F. da Silva, M. A. Batista, and C. A. Z. Barcelos, "Adaptive image retrieval through the use of a genetic algorithm," in Proc. 19th IEEE Int. Conf. Tools WithArtif. Intell. , 2007, pp. 557–564.
  20. Z. Steji, Y. Takama, and K. Hirota, "Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns," Inf. Process. Manage. , vol. 39, no. 1, pp. 1–23, Jan. 2003.
  21. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval, Adisson, 1999.
  22. G. Salton and M. H. McGill. Introduction to Modern Information Retrieval, McGraw-Hill, 1983.
  23. T. -C. Lu and C. -C. Chang, "Color image retrieval technique based on color features and image bitmap," Inf. Process. Manage. , vol. 43, no. 2, pp. 461–472, Mar. 2007.
  24. T. -W. Chiang and T. -W. Tsai, "Content-based image retrieval via the multiresolution wavelet features of interest," J. Inf. Technol. Appl. , vol. 1, no. 3, pp. 205–214, Dec. 2006.
  25. Chih-Chin Lai, "A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm,"IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT/0018-9456/2011.
  27. XinmeiTian, "Multimedia Visual Reranking – from Ranking Objectives to Learning Strategies" Manuscript received October, 2010, revised February, 2011, accepted April, 2011.
  28. H. Takagi, "Interactive evolutionary computation: Fusion of the capacities of EC optimization and human evaluation," Proc. IEEE, vol. 89, no. 9, pp. 1275–1296, Sep. 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Relevance Feedback (rf) Geneticalgorithm (ga) Image Retrieval (ir)

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