Abstract
In this paper, we propose a new region-based image matching method to find the user defined regions in other images. We use color histogram and SAR (simultaneous autoregressive) model parameters as matching features. We characterize the spatial structure of image region with its block features, and we match the image region in target images with spatial constraints. SAR model was usually used to characterize the spatial interactions among neighboring pixels. But the spectrum of the transition matrix G in the SAR model is not well distributed. Therefore in this paper, we use a regularized SAR model to characterize the spatial interactions among neighboring image blocks, which is based on the solution of a penalized LSE (Least Squares Estimation) for computing SAR model parameters. The experimental results show that our method is effective.
Authors are supported by 863(2003AA142140)
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
Ackermann, F.: Digital image correlation-performance and potential application in photogrammetry. Photogrammetry Record 11(64), 429–439 (1984)
Privotera, C.M., Stack, L.W.: Algorithm for Defining Visual Region-of-Interest: Comparison with Eye Fixations. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(9) (2000)
Fauqueur, J., Boujemaa, N.: Region-based Retrieval: Coarse Segmentation with Fine Signature. In: IEEE International Conference on Image Processing, ICIP 2002 (2002)
Gouet, V.: About optimal use of color points of interest for contentbased image retrieval. INRIA RR-No.4439 (April 2002)
Hannah, M.J.: A System for Digital Stereo Image Matching. PERS 12(55), 1765–1770 (1989)
Heipke, C.: Overview of Image Matching Techniques. In: Proceeding of 16th OEEPE Workshop on Application of Digital Photogrammetric Workstation, Lausanne (March 1996)
Itti, L., Koth, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 20(11) (1998)
Mao, J., Jain, A.K.: Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition 25(2), 173–188 (1992)
Moghaddam, B., Biermann, H., Margaritis, D.: Defining image content with multiple regions-of- interest. In: IEEE Workshop on Content-Based Access of Image and Video Libraries (June 1999)
Moravec, H.P.: Visual Mapping by a Robot Rover. In: Proc. Of 6th International Joint Conference of Artificial Intelligence, Tokyo, Japan (1979)
Boujemaa, N., Fsauqueur, J., Gouet, V.: What’s beyond query by Exmaple? INRIA RR-5068 (December 2003)
Rui, Y., Huang, T.S., Chang, S.F.: Image Retrieval: Current Techniques, Promising Directions, and Open Issues. J. Visual Comm. and Image Representation 10(1), 39–62 (1999)
Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5) (1997)
Wang, Y.: Ph D thesis, On the Optimization and Regularization Algorithms for Inverse Problems, Academy of Mathematics and System Sciences, Chinese Academy of Sciences (June 2002)
Wang, Y., Wang, Y., Gao, W., Xue, Y.: A Regularized Simultaneous Autoregressive Model for Texture Classification. In: The 2003 IEEE International Symposium on Circuits and Systems, ISCAS 2003, Bangkok,Thailand, May 25-28, pp. IV/105-IV/108 (2003)
Rukclidge, W.J.: Locating Objects Using the Huasdorff Distance. In: ICCV 1995 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Y., Wang, W., Wang, Y. (2004). A Region Based Image Matching Method with Regularized SAR Model. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30541-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-30541-5_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23974-1
Online ISBN: 978-3-540-30541-5
eBook Packages: Computer ScienceComputer Science (R0)