Abstract
In this paper we present a novel relevance feedback and latent semantic index based cultural relic image retrieval system. First, the optimum weights that can be used for iterative retrieval is computed , then a semantic image link network is constructed to store the semantic correlation information between images, which is obtained from memorized relevance feedbacks. Following image relevance feedback, Latent semantic indexing is applied to image retrieval, which helps saving in storage and estimating the hidden semantic relationship among images. To illustrate the potential of such an approach a prototype image retrieval system has been developed and Preliminary experimental results on a database containing about 2000 images demonstrate the effectiveness of the proposed model.
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
Wei, N., Emre Celebi, M., Geng, G.H.: Content Based Retrieval and Classification of Cultural Relic Images[A]. In: Advances in Neural Networks — ISNN 2005: Second International Symposium on Neural Networks (Proceedings, Part II), Chongqing, China, May 30 - June 1, 2005, pp. 292–298 (2005)
Lu, Y., Hu, C., Zhu, X., et al.: A unified framework for semantics and feature based relevance feedback in image retrieval systems. ACM multimedia (2000)
Baice, L., Senmiao, Y.: A novel relevance feedback method in content-based image retrieval. In: Proceedings of the International Conference on Information Technology: Coding and Computing (2004)
Aksoy, S.: A Probabilistic Similarity Framework for Content-Based Image Retrieval. PhD thesis, University of Washington, Seattle, WA (June 2001)
Han, J., Ngan, K.N., Li, M., Zhang, H.-J.: A memory learning framework for effective image retrieval. IEEE Trans. Image Processing 14(4), 511–524 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Wei, N., Zhou, MQ., Geng, GH. (2007). Relevance Feedback and Latent Semantic Index Based Cultural Relic Image Retrieval. In: Hui, Kc., et al. Technologies for E-Learning and Digital Entertainment. Edutainment 2007. Lecture Notes in Computer Science, vol 4469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73011-8_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-73011-8_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73010-1
Online ISBN: 978-3-540-73011-8
eBook Packages: Computer ScienceComputer Science (R0)