Relevance Feedback Using RBF for Content Based Image Retrieval

Abstract:

Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. However, semantically related images are often scattered across several visual clusters. This leads to adapting multiple queries to represent a query in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space. In this paper, a new content-based image retrieval method with relevance feedback technique using RBF neural network learning is proposed. The method transfers the process of relevance feedback into a learning problem of RBF neural network. RBFNN can describe the distribution of positive feedback sample images in feature space with a set of neighboring clusters produced through constructing neural network, for accurately reflecting their semantic relevance. The performance of the method using RBFNN is evaluated on a database of 10,000 images. Experimental results demonstrate the effectiveness of the proposed method.

• Periodical: Advanced Engineering Forum (Volumes 6-7)
• Pages: 1150-1155
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.6-7.1150
• Online since: September 2012
• Authors: Gui Zhi Li, Chang Sheng Zhou, Wei Wang, Ya Hui Liu
• Keywords: Image Retrieval, RBF, Relevance Feedback

References

[1] Y. Rui T. S. Huang, M. Ortega, S. Mehrotra, Relevance feedback: a power tool for interactive content-base image tetrieval, IEEE Transactions on Circuits and Video Technology, vol 8, no. 5, pp.644-655, (1998).

DOI: 10.1109/76.718510

[2] K. Chandramouli, T. Kliegr, J. Nemrava, V. Svatek, and E. Izquierdo, Query refinement and user relevance feedback for contextualized image retrieval, In Proceeding of Visual Information Engineering, pp.453-458, (2008).

DOI: 10.1049/cp:20080356

[3] M.M. Rahman, P. Bhattacharya, B.C. Desai, A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback, IEEE Transactions on Information Technology in Biomedicine, vol 11, No. 1, pp.58-69, (2007).

DOI: 10.1109/titb.2006.884364

[4] Y. Ishikawa, R. Subramanya, MindReader: query database through multiple examples, Proc. Of Int. Conf. on Very Large Data Bases, New York, USA, (1998).

[5] P. Muneesawang and L. Guan, Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture, IEEE Trans. on Neural Networks, vol. 13, no. 4, pp.821-834, July (2002).

DOI: 10.1109/tnn.2002.1021883

[6] R.J. Liu, Y.H. Wang, T. Baba, D. Masumoto, S. Nagata, SVM-based active feedback in image retrieval using clustering and unlabeled data, Pattern Recognition, vol. 41, pp.2645-2655, (2008).

DOI: 10.1016/j.patcog.2008.01.023