Abstract
In this paper we propose a new method for image retrieval with relevance feedback based on eliciting preferences from the decision-maker acquiring visual information from an image database. The proposed extension of the common approach to image retrieval with relevance feedback allows it to be applied to objects with non-homogenous colour and texture. This has been accomplished by the algorithms, which model user queries by an RBF neural network. As an example of application of this approach, we have used a content-based search in an atlas of species. An experimental comparison with the commonly used content-based image retrieval approach is presented.
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References
Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40(1), 262–282 (2007)
Rui, Y., Huang, T.S., Mehrotra, S., Ortega, M.: A Relevance Feedback Architecture in Content-based Multimedia Information Retrieval Systems. In: Proc. of IEEE Workshop on Content-based Access of Image and Video Libraries, in conjunction with CVPR 1997, Puerto Rico, June 20, 1997, pp. 82–89 (1997)
Ishikawa, Y., Subramanya, R., Faloutsos, C.: MindReader: Querying databases through multiple examples. In: 24th VLDB Conference, New York (1998)
Smith, J.R., Chang, S.F.: VisualSEEk: a fully automated content-based image query system. In: Proc. ACM Intern. Conf. Multimedia, Boston, MA (May 1996)
Faloutsos, C., et al.: Efficient and Effective Querying by Image Content. Journal of Intelligent Information Systems 3(3-4) (July 1994)
Chuang, S.C., et al.: A Multiple-Instance Neural Networks Based Image Content Retrieval System. In: Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC) (2006)
Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3, 246–257 (1991)
Cheng, P.C., Chien, B.C., Ke, H.R., Yang, W.P.: A two-level relevance feedback mechanism for image retrieval. In: Expert Systems with Applications (to appear, 2008)
Doulamis, N., Doulamis, A.: Evaluation of relevance feedback schemes in content-based in retrieval systems. Signal Processing: Image Communication 21(4), 334–357 (2006)
Rui, Y., Huang, T.S., Mehrotra, S.: Relevance Feedback Techniques in Interactive Content-Based Image Retrieval. In: Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, January 24-30, 1998, pp. 25–36 (1998)
Skulimowski, A.M.J., Rotter, P.: Preference elicitation, reference sets and relevance feedback. In: 65th Meeting of the European Working Group Multiple Criteria Decision Aid, Poznan, Poland, April 12-14 (2007)
Rotter, P.: Application of multicriteria optimization methods in image interpretation (in Polish). PhD Thesis, AGH-University of Science and Technology, Krakow (2004)
Skulimowski, A.M.J.: Methods of Multicriteria Decision Support Based on Reference Sets. In: Caballero, R., Ruiz, F., Steuer, R. (eds.) Advances in Multiple Objective and Goal Programming. Lecture Notes in Econ. and Math. Systems, vol. 455, pp. 282–290. Springer, Heidelberg (1997)
Lew, M.S.: Principles of Visual Information Retrieval. Springer, London (2001)
Müller, H., Müller, W., Marchand-Maillet, S., Squire, D.M.: Strategies for positive and negative relevance feedback in image retrieval. In: Proc. of the International Conference on Pattern Recognition (ICPR 2000), Barcelona, Spain, September 3-8, 2000. Computer Vision and Image Analysis, vol. 1, pp. 1043–1046 (2000)
Rocchio, J.J.: Relevance Feedback in Information Retrieval. In: Salton, G. (ed.) The SMART Retrieval System – Experiments in Automatic Document Processing, pp. 313–323. Prentice Hall, Englewood Cliffs (1971)
Ma, W.Y., Manjunath, B.S.: Edge flow: a framework of boundary detection and image segmentation. In: Proc. IEEE International Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, June 1997, pp. 744–749 (1997)
Tadeusiewicz, R.: Intelligent Web mining for semantically adequate images. In: Węgrzyn-Wolska, K.M., Szczepaniak, P.S. (eds.) Advances in intelligent Web mastering : Proceedings of the 5th Atlantic Web Intelligent Conference – AWIC 2007, Fontainebleau, France, June 25–27, 2007. Advances in Soft Computing, vol. 43, pp. 3–10. Springer, Heidelberg (2007)
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Rotter, P., Skulimowski, A.M.J. (2008). A New Approach to Interactive Visual Search with RBF Networks Based on Preference Modelling. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_82
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DOI: https://doi.org/10.1007/978-3-540-69731-2_82
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