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DRM: Dynamic region matching for image retrieval using probabilistic fuzzy matching and boosting feature selection. (English) Zbl 1140.68376

Summary: This paper considers the semantic gap in content-based image retrieval from two aspects: (1) irrelevant visual contents (e.g. background) scatter the mapping from image to human perception; (2) unsupervised feature extraction and similarity ranking method can not accurately reveal users’ image perception. This paper proposes a novel region-based retrieval framework-dynamic region matching (DRM) to bridge the semantic gap. (1) To address the first issue, a probabilistic fuzzy region matching algorithm is adopted to retrieve and match images precisely at object level, which copes with the problem of inaccurate segmentation. (2) To address the second issue, a “FeatureBoost” algorithm is proposed to construct an effective “eigen” feature set in relevance feedback (RF) process. And the significance of each region is dynamically updated in RF learning to automatically capture users’ region of interest (ROI). (3) User’s retrieval purpose is predicted using a novel log-learning algorithm, which predicts users’ retrieval target in the feature space using the accumulated user operations. Extensive experiments have been conducted on Corel image database with over 10,000 images. The promising experimental results reveal the effectiveness of our scheme in bridging the semantic gap.

MSC:

68P20 Information storage and retrieval of data
68U10 Computing methodologies for image processing

Software:

QBIC; NeTra
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Full Text: DOI

References:

[1] Smeuldes A., Worring M., Santini S., Gupta A. and Jain R. (2000). Content-based image retrieval at the end of early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12): 1349–1380 · Zbl 05112814 · doi:10.1109/34.895972
[2] Carson, C., Belongie, S., Greenspan, H., Malik, J.: Region-based image querying. In: Proceeding of IEEE Workshop on Content-Based Access of Image and Video Libraries, pp. 42–49 (1997)
[3] Niblack W., Barber R., Equitz W., Flickner M.D., Glasman E.H., Petkovic D., Yanker P., Faloutsos C. and Taubin G. (1993). The QBIC project: Querying images by content using color, texture and shape. Proceeding of Storage and Retr. Image Video Database 1908: 1723–1787
[4] Pentland A. and Picard R. (1996). Sclaroff: Photobook: Content-based manipulation of image databases. Int. J. Comput. Vis. 18(3): 233–254 · Zbl 05475547 · doi:10.1007/BF00123143
[5] Smithet, J.R., Chang, S.F.: SaFe: A general framework for imtegrated spatial and feature image search. In: IEEE First Workshop on Multimedia Signal Processing, pp. 301–306 (1997)
[6] Rui Y. and Huang T.S. (1997). Mehortra: Content-based image retrieval with relevance feedback in MARS. Proc. IEEE Int. Conf. Image Process. 2(26–29): 815–818 · doi:10.1109/ICIP.1997.638621
[7] Ma, W.Y., Manjunath, B.S.: Netra: A Toolbox for navigation Large Image Database. Proc. Int. Conf. Image Process. pp. 568–571 (1997)
[8] Yamada, A., Pickerings, M., Jeannin, S., Cieplinski, L.: Multimedia Content Desiption Interface–Part 3: Visual, ISO/IEC 15938–3:2001, ver. 1, 2001
[9] Manjunath B.S., Salembier P. and Sikora T. (2002). Introduction to MPEG-7: Multimedia Content Desiption Interface. Wiley, New York
[10] Kam, A.H., Ng, T.T., Kingsbury, N.G., Fitzgerald, W.J.: Content based image retrieval through object extraction and querying. In: Proceeding of IEEE Workshop on Content-based Access of Image and Video Libraries, pp. 91–95 (2000)
[11] Moghaddam B., Biermann H. and Margaritis D. (2000). Image retrieval with local and spatial queries. Proc. IEEE Int. Conf. Image Process. 2: 542–545
[12] Ko, B.C., Lee, H.S., Byun, H.: Region-based image retrieval system using efficient feature desiption. In: Proceeding of 15th International Conference on Pattern Recognition vol. 4, pp. 283–286 (2000)
[13] Wang, W., Song, Y., Zhang, A.: Semantics retrieval by content and context of image regions. Proceeding of the 15th International Conference on Vision Interface, Calgary, Canada (2002)
[14] Chen, Y., Wang, J.Z.: A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), Sept 2002
[15] Kushki A., Androutsos P., Plataniotis K.N. and Venetsanopoulos A.N. (2004). Retrieval from artistic repositories using a decision fusion framework. IEEE Trans. Image Process. 13(2): 277–292 · Zbl 05453058 · doi:10.1109/TIP.2003.821350
[16] Zhang, R., Zhang, Z.: Hidden semantic discovery in region based image retrieval. Proc. Int. Conf. Comput. Vis. Pattern Recognit. 2, II-996–II-1001 July 2004
[17] Li, J., Wang, J.Z., Widerhold, G.: IRM: Integrated region matching for image retrieval. In: Proceeding of ACM Multimedia, Los Angeles, CA, pp. 147–156 (2000)
[18] Wang T., Rui Y. and Sun J.-G. (2004). Constraint based region matching for image retrieval. Int. J. Comput. Vis. 56(1/2/3): 37–45 · Zbl 02060507 · doi:10.1023/B:VISI.0000004831.53436.88
[19] Gong, Y., Zhang H.J., Chua, T.C.: An image database system with content capturing and fast image indexing abilities. Proc. IEEE International Conference on Multimedia Computing and Systems, Boston, pp.121–130, 14–19 May 1994
[20] Buckley, C., Salton, G.:Optimization of relevance feedback weights. Proceedings of SIGIR’95
[21] Rocchio J.J. Jr. (1971). Relevance feedback in information retrieval. In: Salton, G. (eds) The SMART Retrieval System: Experiments in Automatic Document Processing, pp 313–323. Prentice-Hall, New Jessey
[22] Kushki A., Androutsos P., Plataniotis K.N. and Venetsanopoulos A.N. (2004). Query feedback for interactive image retrieval. IEEE Trans. Circuits Syst. Video Technol. 14(5): 644–655 · Zbl 05451178 · doi:10.1109/TCSVT.2004.826759
[23] Burges C.J.C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Discov. 2(2): 121–167 · Zbl 05470543 · doi:10.1023/A:1009715923555
[24] Su, Z., Zhang, H., Li, S.: Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces and progressive learning. IEEE Trans. Image Process. 12(3), (2003)
[25] He, X., King, O., Ma, W.Y., Li, M., Zhang, H.J.: Learning asemantic space from user’s relevance feedback for image retrieval. IEEE Trans. Circuits and Syst. Video Technol. 13(1), 39–48 Jan 2003
[26] Cai, D., He, X., Li, Z., Ma, W.-Y., Wen, J.: Hierarchical clustering of WWW image search results using visual, textual and link information. ACM Multimedia 04, New York, USA, pp. 952–959, October 10–16, 2004.
[27] Serra J. (1988). Image Analysis and Mathematical Morphology, vol 2. Academic Press, New York
[28] Fukunaga, K., Hostetler, L.D.: The estimation of gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory IT-21(1), (1975) · Zbl 0297.62025
[29] Comaniciu D. and Meer P. (2002). Mean Shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach.Intell. 24(5): 603–619 · Zbl 05111281 · doi:10.1109/34.1000236
[30] 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(7) July, 2006
[31] Tieu K. and Viola P. (2004). Boosting image retrieval. Int. J. Comput. Vis. 56(1/2/3): 17–36 · Zbl 02060506 · doi:10.1023/B:VISI.0000004830.93820.78
[32] Hertz T., Bar-Hillel A. and Weinshall D. (2004). Learning distance functions for image retrieval. Compu. Vis. Pattern Recognit. 2: 570–577
[33] Hoi, C.-H., Lyu, M.R.: A novel log-based relevance feedback technique in content-based image retrieval. ACM Multimedia 04, pp. 24–31, October 10–16, 2004, New York, USA
[34] He J., Li M., Zhang H., Tong H. and Zhang C (2006). Generalized manifold-ranking based image retrieval. IEEE Trans. Image Proces. 15(10): 3170–3177 · Zbl 05453624 · doi:10.1109/TIP.2006.877491
[35] Stricker M. and Orengo M. (1995). Similarity of color images. SPIE Storage Retr. Image Video Databases 2185: 381–392
[36] Haralick R.M., Shanmugam K. and Dinstein I. (1973). Texture features for image classification. IEEE Trans. Syst. Man Cybern 3: 610–621 · doi:10.1109/TSMC.1973.4309314
[37] Yu, H., Li, M., Zhang, H., Feng, J.: Color texture moments for content-based image retrieval. In: Proc. of IEEE 2002 Inter. Conf. Image Process. (2002)
[38] Rui Y., Huang T.S., Mehrotra S. and Ortega M. (1998). Relevance Feedback: A power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst. Video Technol. 8(5): 644–655 · doi:10.1109/76.718510
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