Grouping of Semantically Similar Image Positions
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.
KeywordsImage analysis segmentation semantics SIFT
- 1.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)Google Scholar
- 2.Lowe, D.: Object recognition from local scale-invariant features. In: Proc. of the International Conference on Computer Vision ICCV, Corfu, pp. 1150–1157 (1999)Google Scholar
- 3.Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 20, 91–110 (2003)Google Scholar
- 4.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)Google Scholar