Abstract
Much attention has been paid to relevant feedback in intelligent computation for social computing, especially in content-based image retrieval which based on WeChat platform for the medical auxiliary. It has a good effect on reducing the semantic gap between high semantics and low semantics of images. There are many kinds of support vector machines (SVM) based relevance feedback methods in image retrieval, but all of them may encounter some problems, such as a small size of sample, an asymmetric positive sample and negative sample as well as a long feedback cycle. To deal with these problems, an improved asymmetric bagging (IAB) relevance feedback algorithm is proposed. Furthermore, we apply a new fuzzy support machine (FSVM) to cooperate with IAB. To solve the over-fitting and real-time problems, we use modified local binary patterns (MLBP) as image features. Finally, experimental results demonstrate that our method performs other methods in terms of improving retrieval precision as well as retrieval efficiency.
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Notes
- 1.
For more information, please refer to: http://mivia.unisa.it/datasets/biomedical.
References
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)
Rui, Y., Huang, T.S., Mehrotra, S.: Content-based image retrieval with relevance feedback in MARS. In: Proceedings of the IEEE International Conference on Image Processing, vol. 2, pp. 815–818 (1997)
Revalillo-Herraez, M.A., Ferri, F.J.: An improved distance-based relevanced feedback strategey for image retrieval. Image Vis. Comput. 31, 704–713 (2013)
Wei, C.L., Zong, Y.C., Ke, S.-W., Tsai, C.-F.: The effect of low-level image features on pseudo relevance feedback. Neurocomputing 166, 26–37 (2015)
Wu, H., Fang, H.: An incremental approach to efficient pseudo-relevance feedback. In: ACM SIGIR Conference on Research and Development information Retrieval, pp. 553–562 (2013)
Xiang, Y.W., Hong, Y.Y., Yong, W.L.: A new SVM-based active feedback scheme for image retrieval. Eng. Appl. Artif. Intell. 37, 43–53 (2015)
Wang, X.-Y., Chen, J.-W., Yang, H.-Y.: A new integrated SVM classifiers for relevance feedback content-based image retrieval uing EM parameter estimation. Appl. Soft Comput. 11, 2287–2804 (2011)
Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1088–1099 (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Kui, W., Kim, H.Y.: Fuzzy SVM for content-based image retrieval: a pseudo-label support vector machine framework. IEEE Comput. Intell. Mag. 1(2), 10–16 (2006)
Wu, H., Lu, H., Ma, S.D.: A practical SVM based algorithm for ordinal regression in image retrieval. In: Proceedings of ACM Multimedia, pp. 612–621. Berkeley, USA (2003)
Zhou, Z.-H., Chen, K.-J., Jiang, Y.: Exploiting unlabeled data in content-based image retrieval. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 525–536. Springer, Heidelberg (2004)
Bahler, D., Navarro, L.: Methods for combining heterogeneous sets of classifiers. In: Proceedings of the 17th National Conference American Association for Artificial Intelligence (2000)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distribution. Pattern Recogn. 29, 51–59 (1996)
Nanni, L., Lumini, A.: Region boost learning for 2D + 3D based face recognition. Pattern Recogn. Lett. 28(15), 2063–2070 (2007)
Nanni, L., Lumini, A.: A reliable method for cell phenotype image classification. Artif. Intell. Med. 43(2), 87–97 (2008)
Nanni, L., Lumini, A.: Local binary patterns for a hybrid fingerprint matcher. Pattern Recogn. 41(11), 3461–3466 (2008)
Liao, S., Chung, A.C.S.: Face recognition by using elongated local binary patterns with average maximum distance gradient magnitude. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 672–679. Springer, Heidelberg (2007)
Hafiane, A., Seetharaman, G., Palaniappan, K., Zavidovique, B.: Rotationally invariant hashing of median binary patterns for texture classification. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 619–629. Springer, Heidelberg (2008)
Naresh, Y., Nagendraswamy, H.: Classification of medicinal plants: an approach using modified LBP with symbolic representation. Neurocomputing 173, 1789–1797 (2015)
Zhang, W., Shan, H., Chen, X., Gao, W.: Local gabor binary patterns based on mutual information for face recognition. Int. J. Image Graph. 7(4), 777–793 (2007)
Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61472161, 61133011, 61402195, 61502198, 61303132, 61202308), Science & Technology Development Project of Jilin Province (No. 20140101201JC).
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Wang, Ss., Shao, Yn. (2016). An Improved Asymmetric Bagging Relevance Feedback Strategy for Medical Image Retrieval. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_16
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