Abstract
Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. In recent years, the relevance feedback scheme has been applied to Content-Based Image Retrieval (CBIR) and effective results have been obtained. However, most of the conventional feedback process has the problem that updating of metric space is hard to understand visually. In this paper, we propose a CBIR algorithm using Self-Organizing Map (SOM) with visual relevance feedback scheme. Then a pre-learning algorithm in the visual relevance feedback is proposed for constructing user-dependent metric space. We show the effectiveness of the proposed technique by subjective evaluation experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Feng, D., Siu, W.C., Zhang, H.J. (eds.): Multimedia Information Retrieval and Management. Springer, Heidelberg (2003)
Rocchio Jr., J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)
MacArthur, S.D., Brodley, C.E., Shyu, C.-R.: Relevance feedback decision trees in content-based image retrieval. In: Proc. IEEE Workshop on Content-based Access of Inage and Video Libraries, pp. 68–72 (2000)
Vasconcelos, N., Lippman, A.: Learning from user feedback in image retrieval system. In: Proc. NIPS 1999 (1999)
Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: Proc, 9th ACM conference on multimedia (2001)
Rui, Y., Huang, T.S., Mehrotra, S.: Content-based image retrieval with relevance feedback: in MARS. In: Proc. IEEE ICIP, pp. 815–818 (1997)
Hesterkamp, D.R., Peng, J., Dai, H.K.: Feature relevance learning with query shifting for content-based image retrieval. In: Proc. ICPR 2000, pp. 250–253 (2000)
Ishikawa, Y., Subramanya, R., Faloutsos, C.: Mindreader: Querying databases through multiple examples. In: Proc. 24th Int. Conf. Very Large Data Bases (1998)
Kohonen, T.: Self-organization and Associative Memory, 3rd edn. Springer, Heidelberg (1989)
Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: PicSOM – content-based image retrieval with self-organizing maps. Pattern Recognition Letters 21(13-14), 1199–1207 (2000)
Koskela, M., Laaksonen, J., Oja, E.: Implementing relevance feedback as convolutions of local neighborhoods on Self-Organizing Maps. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 981–986. Springer, Heidelberg (2002)
Asami, S., Kotera, H.: Content-Based Image Retrieval System By Multi-Dimensional Feature Vectors. In: Proc. IS&T’s NIP19, pp. 841–845 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nishikawa, T., Horiuchi, T., Kotera, H. (2004). SOM-Based Sample Learning Algorithm for Relevance Feedback in CBIR. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30541-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-540-30541-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23974-1
Online ISBN: 978-3-540-30541-5
eBook Packages: Computer ScienceComputer Science (R0)