Abstract
Features from the Scale Invariant Feature Transformation (SIFT) are widely used for matching between spatially or temporally displaced images. Recently a topology on the SIFT features of a single image has been introduced where features of a similar semantics are close in this topology. We continue this work and present a technique to automatically detect groups of SIFT positions in a single image where all points of one group possess a similar semantics. The proposed method borrows ideas and techniques from the Color-Structure-Code segmentation method and does not require any user intervention.
Chapter PDF
Similar content being viewed by others
References
Hering, N., Schmitt, F., Priese, L.: Image understanding using self-similar sift features. In: International Conference on Computer Vision Theory and Applications (VISAPP), Lisboa, Portugal (to be published, 2009)
Lowe, D.: Object recognition from local scale-invariant features. In: Proc. of the International Conference on Computer Vision ICCV, Corfu, pp. 1150–1157 (1999)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2003)
Rehrmann, V., Priese, L.: Fast and robust segmentation of natural color scenes. In: Chin, R.T., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1351, pp. 598–606. Springer, Heidelberg (1997)
Slot, K., Kim, H.: Keypoints derivation for object class detection with sift algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS, vol. 4029, pp. 850–859. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Priese, L., Schmitt, F., Hering, N. (2009). Grouping of Semantically Similar Image Positions. In: Salberg, AB., Hardeberg, J.Y., Jenssen, R. (eds) Image Analysis. SCIA 2009. Lecture Notes in Computer Science, vol 5575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02230-2_74
Download citation
DOI: https://doi.org/10.1007/978-3-642-02230-2_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02229-6
Online ISBN: 978-3-642-02230-2
eBook Packages: Computer ScienceComputer Science (R0)
