Introduction
The subject of this chapter is semantic alignment. This is the conversion of multiple input data or measurements which do not refer to the same object, or phenomena, to a common object or phenomena. The reason for performing semantic alignment is that different inputs can only be fused together if the inputs refer to the same object or phenomena. In general, if the observations have been made by sensors of the same type, then the observations should refer to the same object or phenomena. In this case, no semantic alignment is required, although radiometric normalization may be required.
This is a preview of subscription content, log in via an institution to check access.
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
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Patt. Anal. Mach. Intell. 24, 509–522 (2002)
Brand, M., Huang, K.: A unifying theorem for spectral embedding and clustering. In: Ninth Int. Conf. Art Intell. Stat. (2002)
Dudoit, S., Fridlyand, J.: Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19, 1090–1099 (2003)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the Nystrom method. IEEE Trans. Patt. Anal. Mach. Intell. 26, 214–225 (2004)
Fred, A.L.N.: Finding Consistent Clusters in Data Partitions. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 309–318. Springer, Heidelberg (2001)
Fred, A.L.N., Jain, A.K.: Combining multiple clusterings using evidence accumulation. IEEE Trans. Patt. Anal. Mach. Intell. 27, 835–850 (2005)
Franek, L., Abdala, D.D., Vega-Pons, S., Jiang, X.: Image Segmentation Fusion Using General Ensemble Clustering Methods. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part IV. LNCS, vol. 6495, pp. 373–384. Springer, Heidelberg (2011)
Ghaemi, R., Sulaiman, M.N., Ibrahim, H., Mustapha, N.: A survey: clustering ensembles techniques. World Acad. Sci. Eng. Tech. 50, 636–645 (2009)
Jain, A.K.: Data clustering: 50 years beyond K-means. Patt. Recogn. Lett. 31, 651–666 (2010)
Jia, J., Liu, B., Jiao, L.: Soft spectral clustering ensemble applied to image segmentation. Front. Comp. Sci. China 5, 66–78 (2004)
Li, T., Ogihara, M., Ma, S.: On combining multiple clusterings: an overview and a new perspective. Appl. Intell. (2010)
Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Research Logistics 52, 7–21 (2005)
Mignotte, M.: Segmentation by fusion of histogram-based K-means clusters in different color spaces. IEEE Trans. Im. Proc. 17, 780–787 (2008)
Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. Adv. Neural Inform. Proc. Sys. 14, 849–856 (2001)
Scott, C., Nowak, R.: Robust contour matching via the order preserving assignment problem. IEEE Trans. Image Process. 15, 1831–1838 (2006)
Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Mach. Learn. Res. 3, 583–617 (2002)
Vega-Pons, S., Ruiz-Shulcloper, J.: Int. J. Patt. Recogn. Art Intell. 25, 337–372 (2011)
A comparison of spectral clustering algorithms, Tech. Rept UW-CSE-03-05-01. Dept. Comp. Sci. Eng., Univ. Washington (2003)
von Luxburg, U.: A tutorial on spectral clustering. Stat. Comp. 17, 395–416 (2007)
Wang, Z., Gao, C., Tian, J., Lia, J., Chen, X.: Multi-feature distance map based feature detection of small infra-red targets with small contrast in image sequences. In: Proc. SPIE, vol. 5985 (2005)
Wang, X., Yang, C., You, J.: Spectral aggregation for clustering ensemble. In: Proc. Int. Conf. Patt. Recogn., pp. 1–4 (2008)
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. Adv. Neural. Inform. Proc. Sys. 17, 1601–1608 (2005)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Mitchell, H.B. (2012). Semantic Alignment. In: Data Fusion: Concepts and Ideas. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27222-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-27222-6_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27221-9
Online ISBN: 978-3-642-27222-6
eBook Packages: EngineeringEngineering (R0)