Abstract
The rapid growth of computer technologies and the advent of the World Wide Web have increased the amount and the complexity of multimedia information. A content -based image retrieval (CBIR) system has been developed as an efficient image retrieval tool, whereby the user can provide their query to the system to allow it to retrieve the user’s desired image from the image database. However, the traditional relevance feedback of CBIR has some limitations that will decrease the performance of the CBIR system, such as the imbalance of training-set problem, classification problem, limited information from user problem, and insufficient training set problem. Therefore, in this study, we proposed an enhanced relevance-feedback method to support the user query based on the representative image selection and weight ranking of the images retrieved. The support vector machine (SVM) has been used to support the learning process to reduce the semantic gap between the user and the CBIR system. From these experiments, the proposed learning method has enabled users to improve their search results based on the performance of CBIR system. In addition, the experiments also proved that by solving the imbalance training set issue, the performance of CBIR could be improved.
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
Long, F., Zhang, H., David, D.F.: Fundamentals of content-based image retrieval. In: Multimedia Information Retrieval and Management - Technological Fundamentals and Applications. Springer (2003, 2010)
Tao, D.C., Tang, X.O., Li, X.L., Wu, X.D.: Asymmetric bagging and random space for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 28(7) (July 2006)
Crucianu, M., Ferecatu, M., Boujemaa, N.: Relevance feedback for image retrieval: A short survey. Report of the DELOS2 European Network of Excellence, 6th Framework Programme (October 10, 2004)
Qi, X., Chang, R.: Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 638–649. Springer, Heidelberg (2007)
Das, G., Ray, S.: A comparison of relevance feed-back strategies in CBIR. IEEE (2007)
Rui, Y., Huang, T.S., Ortega, M., Methrotra, S.: Rel-evance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. On Circuits and Systems for Video Technology 8(5), 644–655 (1998)
Hoi, C.H., Chan, C.H., Huang, K.Z., Lyu, M.R., King, I.: Biased support vector machine for relevance feedback in image retrieval. In: Proceedings of Intl. Joint Conf. on Neural Networks (IJCNN 2004), Budapest, Hungary (2004)
Kim, D.H., Song, J.W., Lee, J.H., Choi, B.G.: Sup-port vector machine learning for region-based image retrieval with relevance feedback. ETRI Journal 29(5) (October 2007)
Rui, Y., Huang, T.S.: A novel relevance feedback techniques in image retrieval. In: Proc. 7th ACM Conf. on Multimedia, pp. 67–70 (1999)
Cheng, P.C., Chien, B.C., Ke, H.R., Yang, W.P.: A two-level relevance feedback mechanism for image retrieval. Expert Systems with Applications 34(3), 2193–2200 (2008)
Qin, T., Zhang, X.D., Liu, T.Y., Wang, D.S., Ma, W.Y., Zhang, H.J.: An active feedback framework for image retrieval. Pattern Recognition Letters 29(5), 637–6461 (2008)
Smith, J.R., Chang, S.F.: Automated binary texture feature sets for image retrieval. In: Proc. ICASSP 1996, May 7-10 (1996)
Blekas, K., Likas, A., Galatsanos, N., Lagaris, I.: A Spatially-Constrained Mixture Model for Image Segmentation. IEEE Transactions on Neural Networks 16(2), 494–498 (2005)
Liu, Y., Zhang, D.S., Lu, G.: Region-Based Image Retrieval with High-Level Semantics using Decision Tree Learning. Pattern Recognition Ramakrishna Reddy.Eamani et al. / International Journal of Engineering Science and Technology (IJEST)Â 4(4), 1518 (2012); Recognition 41(8), 2554-2570 (August 2008)
Manjunath, B., Wu, P., Newsam, S., Shin, H.: A texture descriptor for browsing and similarity retrieval. Signal Processing Image Communication (2001)
Ohm, J., Bunjamin, F., Liebsch, W., Makai, B., Mller, K., Smolic, A., Zier, D.: A Set of Visual Feature Descriptors and their Combination in a Low-Level Description Scheme. Signal Processing: Image Communication 16, 157–179 (2000)
Greenspan, H., Dvir, G., Rubner, Y.: Region Correspondence for Image Matching via EMD Flow. IEEE (2000)
Chowdhury, G.G.: Introduction to modern information retrieval. Library Association Publishing, London (1999); Eamani, R.R., et al.: International Journal of Engineering Science and Technology (IJEST)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Pavani, P., Prabha, T.S. (2014). Content Based Image Retrieval Using Machine Learning Approach. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-02931-3_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02930-6
Online ISBN: 978-3-319-02931-3
eBook Packages: EngineeringEngineering (R0)