Abstract
It has been generally acknowledged that relevance feedback in image retrieval can be considered as a two-class learning and classification process. The classifier used is essential to the performance of relevance feedback. In this paper, a RBF neural network is employed during the relevance feedback process. The architecture of the RBF network is automatically determined by the constructive learning algorithm (CLA). The weights in the output layer of the network are learned by Least-mean-square method. Experiment results on 10,000 heterogeneous images demonstrate the proposed CLA-based RBF network can achieve comparable performance with support vector machines and support vector learning based RBF during the relevance feedback process. Furthermore, a practical advantage of the CLA-based RBF network is that the width of Gaussian kernel does not need to manually set while for SVM it need to be predefined according to experience.
The work is supported by National Nature Sciences Foundation of China No. 60135010.
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References
Cox, I. J., Minka, T. P., Papathomas, T.V. and Yianilos, P. N.: The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments, IEEE Transactions on Image Processing, Vol. 9(1), (2000) 20–37
Hong, P., Tian, Q. and Huang, T. S.: Incorporate Support Vector Machines to Content-Based Image Retrieval with Relevance Feedback, IEEE Int’l conf. on Image Processing, Vancouver, Canada, Sep 10–13, (2000)
Ishikawa, Y. and Subramanya, R.: MindReader: Query databases through multiple examples, Proc. of the 24th VLDB conference, New York, (1998)
Rui, Y., Huang, T. S., Ortega, M. and Mehrotra, S.: Relevance feedback: A power tool in interactive content-based image retrieval, IEEE Transaction on Circuits and Systems for Video Technology, Special Issue on Segmentation, Description, and Retrieval of Video Content, 8(5), (1998) 644–655
Schoelkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T. and Vapnik, V.: Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers, IEEE Transcations on Signal Processing, Vol.45:11, (1997) 2758–2765
Schwenker F, Kestler HA, Palm G.: Three learning phases for radial-basis-function networks, Neural Networks, 14(4–5), (2001) 439–458
Stricker M. and Orengo, M., Similarity of color images. in Proc. SPIE Storage and Retrieval for Image and Video Databases. 1995
Tieu, K. and Viola, P.: Boosting image retrieval, Proc. IEEE Conf. Computer Vision and Pattern Recognition, Hilto Head Island, SC. (2000)
Vapnik, V. The nature of statistical learning theory. Springer-Verlag, New York. (1995)
Vasconcelos, N. and Lippman, A.: A Probabilistic Architecture for Contenbased Image Retrieval, Proc. IEEE Computer Vision and Pattern Recognition Conf., Hilton Head, North Carolina, (2000)
Zhang, L. and Zhang, B.: A geometrical representation of McCulloch-Pitts neural model and its application, IEEE Transaction on Neural Networks, 10(4), (1999) 925–929
Zhang, L., Lin, F. Z. and Zhang, B.: A neural network based self-learning algorithm of image retrieval, Chinese Journal of Software, 12(10), (2001) 1479–1485
Zhang, L., Lin, F. Z. and Zhang, B.: Support vector learning for image retrieval, IEEE International Conference on Image Processing, Thessaloniki, Greece. (2001) 721–724
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Fang, Q., Bo, Z., Fuzong, L. (2003). Constructive Learning Algorithm-Based RBF Network for Relevance Feedback in Image Retrieval. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_35
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