Abstract
In this paper, an interactive image retrieval scheme using MPEG-7 visual descriptors is proposed. The performance of image retrieval systems is still limited due to semantic gap, which is created from the discrepancies between the computed low-level features (color, texture, shape, etc.) and user’s conception of an image. As a result, more interest has been created towards development of efficient learning mechanism other than designing sophisticated low-level feature extraction algorithms. A simple relevance feedback mechanism is proposed, that learns user’s interest and updates feature weights based on a fuzzy feature evaluation measure. This has an advantage of handling comparatively small number of samples over those using standard classifiers involving large number of training samples and having more complexity. Extensive experiments have been performed to test to what extent the performance of an image retrieval system can be enhanced further using MPEG-7 standard visual features at minimum cost.
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References
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Cheng, K.O., Law, N.F., Siu, W.C.: Multiscale directional filter bank with applications to structured and random texture retrieval. Pattern Recognition 40(4), 610–621 (2007)
Martínez, J.C., Medina, J.M., Barranco, C.D., Perales, G., Hidalgo, J.M.S.: Retrieving images in fuzzy object-relational databases using dominant color descriptors. Fuzzy Sets and Systems 158(3), 312–324 (2007)
Manjunath, B.S., Salembier, P., Sikora, T.: Introduction to MPEG-7: Multimedia Content description Interface. John Wiley and Sons, Inc., Chichester (2002)
Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 703–715 (2001)
Rui, Y., Huang, T.S., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE transactions on Circuits and Systems for Video technology 8(5), 644–655 (1998)
Yin, P.Y., Bhanu, B., Chang, K.C., Dong, A.: Integrating relevance feedback techniques for image retrieval using reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1536–1551 (2005)
Lim, J.H., Jin, J.S.: Combining intra-image and inter-class semantics for consumer image retrieval. Pattern Recognition 38(6), 847–864 (2005)
Jin, Z., King, I., Li, X.Q.: Content-based retrieval by relevance feedback. In: Laurini, R. (ed.) VISUAL 2000. LNCS, vol. 1929, pp. 521–529. Springer, Heidelberg (2000)
Ves, E.D., Domingo, J., Ayala, G., Zuccarello, P.: A novel bayesian framework for relevance feedback in image content-based retrieval systems. Pattern Recognition 39(9), 1622–1632 (2006)
Qian, F., Zhang, B., Lin, F.: Constructive learning algorithm-based rbf network for relevance feedback in image retrieval. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 352–361. Springer, Heidelberg (2003)
He, X., King, O., Ma, W., Li, M., Zhang, H.J.: Learning a semantic space from user’s relevance feedback for image retrieval. IEEE transactions on Circuits and Systems for Video technology 2003 13(1) (2003)
Banerjee, M., Kundu, M.K., Das, P.K.: Image Retrieval with Visually Prominent Features using Fuzzy Set Theoretic Evaluation. In: IEE International Conference on Visual Information Engineering VIE 2006, India, pp. 298–303 (2006)
Pal, S.K., Chakraborty, B.: Intraclass and interclass ambiguities (fuzziness) in feature evaluation. Pattern Recognition Letters 2, 275–279 (1984)
Pal, S.K., Majumder, D.D.: Fuzzy mathematical Approach to Pattern Recognition. Willey Eastern Limited, New York (1985)
King, I., Jin, Z.: Integrated probability function and its application to content-based image retrieval by relevance feedback. Pattern Recognition 36, 2177–2186 (2003)
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Kundu, M.K., Banerjee, M., Bagrecha, P. (2009). Interactive Image Retrieval in a Fuzzy Framework. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2009. Lecture Notes in Computer Science(), vol 5571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02282-1_31
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DOI: https://doi.org/10.1007/978-3-642-02282-1_31
Publisher Name: Springer, Berlin, Heidelberg
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