© The Institution of Engineering and Technology
This study proposes a new method for content-based image retrieval by finding an optimal classifier. The optimal classifier is achieved by a new active learning support vector machine (SVM) which combines the model selection with the active learning. The unlabelled samples close to the boundary of the SVM classifier are selected based on the feature similarity for the active learning, and the adaptive regularisation is used to select the optimal model. The combination of model selection with active learning accelerates the convergence of the classifier. The new method can improve the image retrieval accuracy and reduce the time consumption. The experimental results show that the proposed method has a better performance with fewer samples and less time consumption for image retrieval.
References
-
-
1)
-
11. Niu, B., Cheng, J., Bai, X., et al: ‘Asymmetric propagation based batch mode active learning for image retrieval’, Signal Process., 2013, 93, (2013), pp. 1639–1650 (doi: 10.1016/j.sigpro.2012.07.018).
-
2)
-
8. Liu, R., Wang, Y.: ‘SVM-based active feedback in image retrieval using clustering and unlabeled data’, Pattern Recognit., 2008, 41, (8), pp. 2645–2655 (doi: 10.1016/j.patcog.2008.01.023).
-
3)
-
13. Wang, X., Chen, J., Yang, H.: ‘A new integrated SVM classifiers for relevance feedback content-based image retrieval using EM parameter estimation’, Appl. Soft Comput., 2011, 11, (2), pp. 2787–2804 (doi: 10.1016/j.asoc.2010.11.009).
-
4)
-
1. Tong, S., Chang, E.: ‘Support vector machine active learning for image retrieval’. Proc. Ninth ACM Int. Conf. on Multimedia, Ottawa, Canada, October 2001, pp. 107–118.
-
5)
-
2. Rubens, N., Kaplan, D., Sugiyama, M.: ‘Active learning in recommender systems’, in Kantor, P.B., Ricci, F., Rokach, L., Shapira, B. (Eds.): ‘Recommender systems handbook’ (Springer, 2011), pp. 735–767.
-
6)
-
20. Huiskes, M., Lew, M.: ‘Performance evaluation of relevance feedback method’. Proc. 2008 Int. Conf. on Content-Based Image and Video Retrieval (CIVR 08), ACM, New York, July 2008, pp. 239–248.
-
7)
-
7. Hastie, T., Rosset, S., Tibshirani, R., et al: ‘The entire regularization path for the support vector machine’, J. Mach. Learn. Res., 2004, 5, (2004), pp. 1391–1415.
-
8)
-
19. Wang, X., Li, Y., Yang, H., et al: ‘An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification’, Neurocomputing, 2014, 127, (15), pp. 214–230 (doi: 10.1016/j.neucom.2013.08.007).
-
9)
-
9. Steven, C., Jin, R., Zhu, J., et al: ‘Semi-supervised SVM batch mode active learning and its applications to image retrieval’, ACM Trans. Inf. Syst., 2009, 27, (3), pp. 1–29.
-
10)
-
10. Hu, L., Lu, S., Wang, X.: ‘A new and informative active learning approach for support vector machine’, Inf. Sci., 2013, 244, (2013), pp. 142–160.
-
11)
-
6. Vapnik, V.: ‘Statistic learning theory’ (Wiley-Interscience, USA, 1998).
-
12)
-
12. Reitmaier, T., Calma, A., Sick, B.: ‘Transductive active learning – a new semi-supervised learning approach based on iteratively refined generative models to capture structure in data’, Inf. Sci., 2015, 293, (2015), pp. 275–298 (doi: 10.1016/j.ins.2014.09.009).
-
13)
-
5. Wang, Z., Yan, S., Zhang, C.: ‘Active learning with adaptive regularization’, Pattern Recognit., 2011, 44, (2011), pp. 2375–2383 (doi: 10.1016/j.patcog.2011.03.008).
-
14)
-
3. Wang, X., Zhang, B., Yang, H.: ‘Active SVM-based relevance feedback using multiple classifiers ensemble and features re-weighting’, Eng. Appl. Artif. Intell., 2013, 26, (2013), pp. 368–381 (doi: 10.1016/j.engappai.2012.05.008).
-
15)
-
4. Sugiyama, M., Rubens, N.: ‘Active learning with model selection in linear regression’. Proc. SIAM Int. Conf. on Date Mining (SDM), Atlanta, GA, USA, April 2008, pp. 518–529.
-
16)
-
17. Griffin, G., Holub, A., Perona, P.: ‘Caltech-256 object category dataset’. , 2007.
-
17)
-
14. Lughofer, E.: ‘Hybrid active learning for reducing the annotation effort of operators in classification systems’, Pattern Recognit., 2012, 45, (2), pp. 884–896 (doi: 10.1016/j.patcog.2011.08.009).
-
18)
-
16. Lin, Y.: ‘Support vector machines and the Bayes rule in classification’, Data Min. Knowl. Discov., 2002, 6, (3), pp. 259–275 (doi: 10.1023/A:1015469627679).
-
19)
-
18. Shen, X., Ju, Sh., Cho, S., et al: ‘Mining user hidden semantic from image content for image retrieval’, J. Vis. Commun. Image Represent., 2008, 19, (2008), pp. 145–164 (doi: 10.1016/j.jvcir.2007.04.009).
-
20)
-
15. Nello, C., John, Sh.: ‘An introduction to support vector machine and other kernel-based learning methods’ (Chinese Language Edition Published by Publishing House of Electronics Industry, 2004).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2015.0101
Related content
content/journals/10.1049/iet-cvi.2015.0101
pub_keyword,iet_inspecKeyword,pub_concept
6
6