Adaptive learning region importance for region-based image retrieval
- Author(s): Xiaohui Yang 1 ; Feiya Lv 1 ; Lijun Cai 1 ; Dengfeng Li 1
-
-
View affiliations
-
Affiliations:
1:
School of Mathematics and Information Sciences, Institute of Applied Mathematics, Henan University, Kaifeng 475000, Henan, People's Republic of China
-
Affiliations:
1:
School of Mathematics and Information Sciences, Institute of Applied Mathematics, Henan University, Kaifeng 475000, Henan, People's Republic of China
- Source:
Volume 9, Issue 3,
June 2015,
p.
368 – 377
DOI: 10.1049/iet-cvi.2014.0119 , Print ISSN 1751-9632, Online ISSN 1751-9640
This study addresses the issue of region representation in region-based image retrieval (RBIR). In order to reduce the user's burden of selecting the region of interest, a statistical index called visual region importance (RI) is constructed to describe the region. By learning from user's current and historical feedback information, visual RI can be automatically updated and semantic RI can be obtained. Furthermore, adaptive learning RI and memory learning RI (MLRI) techniques for RBIR system have been presented. Specifically, the MLRI can mitigate the negative influence of interference regions well. Extensive experiments on the Corel-1000 dataset and the Caltech-256 dataset demonstrate that the proposed frameworks are effective, are robust and achieve significantly better performance than the other existing methods.
Inspec keywords: feature extraction; content-based retrieval; learning (artificial intelligence); image retrieval
Other keywords: semantic RI; interference regions; Caltech-256 dataset; MLRI techniques; statistical index; adaptive learning RI techniques; RBIR system; visual region importance; region-based image retrieval; feedback information; Corel-1000 dataset; region representation issue; memory learning RI techniques; adaptive learning region importance
Subjects: Knowledge engineering techniques; Spatial and pictorial databases; Image recognition; Computer vision and image processing techniques
References
-
-
1)
-
4. Zdziarski, Z., Dahyot, R.: ‘Feature selection using visual saliency for content-based image retrieval’. IET Irish Signals and Systems Conf. (ISSC 2012), 2012, p. 46.
-
-
2)
-
21. Tao, W., Jin, H., Zhang, Y.: ‘Color image segmentation based on mean shift and normalized cuts’, IEEE Trans. Syst. Man Cybern., 2007, 37, (5), pp. 1382–1389 (doi: 10.1109/TSMCB.2007.902249).
-
-
3)
- Y. Rui , T.S. Huang , M. Ortega , S. Mehrotra . Relevance feedback: a powerful tool in interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Technol. , 5 , 644 - 655
-
4)
-
10. Ducksbury, P.G., Varga, M.J.: ‘Region based image content descriptors and representation’. Sixth Int. Conf. on Image Processing and its Applications’, IET Computer Vision, 1997, pp. 561–565.
-
-
5)
-
13. Jing, F., Zhang, B., Lin, F.Z., Ma, W.Y., Zhang, H.J.: ‘A novel region-based image retrieval method using relevance feedback’. 2001 ACM Workshop on Multimedia: Multimedia Information Retrieval, Ottawa, Canada, September–October 2001, pp. 28–31.
-
-
6)
-
11. Parashar, A.: ‘Region based image retrieval systems’. National Conf. on Signal and Image Processing Applications’, IET Computer Vision, 2009, p. 55.
-
-
7)
-
28. Wood, M.E.J., Thomas, B.T., Campbell, N.W.: ‘Iterative refinement by relevance feedback in content based digital image retrieval’ (ACM Multimedia, New York, 1998), pp. 13–20.
-
-
8)
-
24. Bulo, S.R., Rabbi, M., Pelillo, M.: ‘Content-based image retrieval with relevance feedback using random walks’, Pattern Recognit., 2011, 44, (9), pp. 2109–2122 (doi: 10.1016/j.patcog.2011.03.016).
-
-
9)
-
16. Li, J., Wang, J.Z., Wiederhold, G.: ‘IRM: integrated region matching for image retrieval’. Eighth ACM Int. Conf. on Multimedia, Los Angeles, CA, USA, October 2000, 8 (2), pp. 147–156.
-
-
10)
- D. Comaniciu , P. Meer . Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. , 5 , 603 - 619
-
11)
-
7. Ferreira, C.D., Asntos, J.A., Torres, R.S., Goncalves, M.A., Rezende, R.C., Fan, W.: ‘Relevance feedback based on genetic programming for image retrieval’, Pattern Recognit., 2011, 32, (1), pp. 27–37 (doi: 10.1016/j.patrec.2010.05.015).
-
-
12)
-
17. Yang, X.H., Cai, L.J.: ‘Adaptive region matching for region-based image retrieval by constructing region importance index’, IET Comput. Vis., 2013, 8, (2), pp. 1–11 (doi: 10.1007/s11263-013-0629-9).
-
-
13)
-
3. Lowe, D.: ‘Object recognition from local scale-invariant features’. Proc. of Int. Conf. on Computer Vision, September 1999, pp. 1150–1157.
-
-
14)
-
21. Cord, M., Gosselin, P.H.: ‘Image retrieval using long-term semantic learning’. Proc. Int. Conf. Image Processing, 2006, pp. 2909–2912.
-
-
15)
-
6. Baeza-Yates, R., Ribeiro-Neto, B.: ‘Modern information retrieval’ (Addison-Wesley, ACM Press, New York, 1999).
-
-
16)
-
29. He, X.F., King, O., Ma, W.Y., Li, M.J., Zhang, H.J.: ‘Learning a semantic space from user's relevance feedback for image retrieval’, IEEE Trans. Circuits, Syst. Video Technol., 2003, 13, (1), pp. 39–48 (doi: 10.1109/TCSVT.2002.808087).
-
-
17)
-
26. Doyle, W.: ‘Operations useful for similarity-invariant pattern recognition’, J. ACM, 1962, 9, (2), pp. 259–267 (doi: 10.1145/321119.321123).
-
-
18)
-
35. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: ‘Contour detection and hierarchical image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, pp. 898–916 (doi: 10.1109/TPAMI.2010.161).
-
-
19)
-
27. Wang, J.Z., Du, Y.P.: ‘Scalable integrated region-based image retrieval using IRM and statistical clustering’. Proc. First ACM/IEEE-CS Joint Conf. Digital Libraries., 2001, pp. 268–277.
-
-
20)
-
1. Kato, T.: ‘Database architecture for content-based image retrieval’, Image Storage Retr. Syst. SPIE, 1992, 1662, pp. 112–123 (doi: 10.1117/12.58497).
-
-
21)
- A. Pentland . Tools for content based image retrieval. Int. J. Comput. Vis. , 3 , 233 - 254
-
22)
-
14. Jing, F., Li, M.J., Zhang, H.J., Zhang, B.: ‘Relevance feedback in region-based image retrieval’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (5), pp. 672–681 (doi: 10.1109/TCSVT.2004.826775).
-
-
23)
-
2. Liu, G.H., Yang, J.Y.: ‘Content-based image retrieval using color difference histogram’, Pattern Recognit., 2013, 46, (1), pp. 188–198 (doi: 10.1016/j.patcog.2012.06.001).
-
-
24)
-
8. Han, J., Ngan, K., Li, M., Zhang, H.: ‘A memory learning framework for effective image retrieval’, IEEE Trans. Image Process., 2005, 14, (4), pp. 511–524 (doi: 10.1109/TIP.2004.841205).
-
-
25)
- C. Carson , S. Belongie , H. Greenspan , J. Malik . Blobworld: image segmentation using expectation- maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. , 8 , 1026 - 1038
-
26)
-
19. Wang, X.Y., Li, Y.W., Yang, H.Y., Chen, J.W.: ‘An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification’, Pattern Recognit. Neuro Comput., 2014, 127, (15), pp. 214–230.
-
-
27)
-
9. Cai, L.J., Yang, X.H., Li, S.C., Li, D.F.: ‘Relevance feedback based on particle swarm optimization for image retrieval’. Proc. of the 2012 Int. Conf. on Infor. Tech. and Software Engineering Lecture Notes in Electrical Engineering, 2013, no. 212, pp. 749–756.
-
-
28)
-
8. Broilo, M., Natale, D., Francesco, G.B.: ‘A stochastic approach to image retrieval using relevance feedback and particle swarm optimization’, IEEE Trans. Multimed., 2010, 12 (4), pp. 267–277 (doi: 10.1109/TMM.2010.2046269).
-
-
29)
- J.Z. Wang , J. Li , G. Wiederhold . SIMPLIcityL: semantic-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. , 9 , 1 - 17
-
1)