Abstract
A novel method for moving video objects recognition is presented in this paper. In our method, support vector machine (SVM) is adopted to train the recognition model. With the trained model, the moving video objects can be recognized based on the shape features extraction. Comparing with the traditional methods, our method is faster, more accurate and more reliable. The experimental results show the competitiveness of our method.
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
Amoid, M.W., Simone, S., et al.: Content-based image retrieval at the end of the early years [J]. IEEE Trans on Pattern Analysis and Machine Intelligence 22(12), 1349–1379 (2000)
Gupta, A., Jain, R.: Visual information retrieval. Communications of the ACM 40(5), 70–79 (1997)
Lee, J.C.M., Jain, A.K.: Special Issue on Image Database. Pattern Recognition 20(4), 537–658 (1997)
Marr, D.: Representing Visual Information. AI Memo 415, Mass. Inst. Technol. AI Lab., Cambridge (1977)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley & Sons, Chichester (1973)
Vapnik, V.: Statistical Learning theory. Wiley, New York (1998)
Bian, Z.Q., Zhang, X.G.: Model Recognition. Tsinghua University Press, Beijing (2000)
Platt, J.C.: Fast Training Support Vector Machines using Sequential Minimal Optimization. In: Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge (1998)
Feng, H.Z.: (2006), http://www.whihr.com.cn/htdocs/downfile/%E5%86%AF%E6%B1%89%E4%B8%AD.ppt
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)
Mangasarian, O.L., Musciant, D.R.: Successive Overrelexation for Support Vector Machines. IEEE Trans. Neural Network 10(5), 1032–1037 (1999)
Bredensteiner, E.J., Bennett, K.P.: Multicategory Classification by Support Vector Machines. Computational Optimizations and Applications, pp. 53–79 (1999)
Westen, J., Watkins, C.: Multi-class Support Vector Machines. In: Proceedings of ESANN 1999 (1999)
Grammer, K., Singer, Y.: On the learnability and design of output codes for multiclass problems. In: Computational Learning Theory, pp. 35–46 (2000)
Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machine: An Application to Face Detection. In: Proceedings of CVPR 1997 (1997)
Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001)
Deng, Y.N., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(8), 800–810 (2001)
Cheng, J.K., Huang, T.S.: Image registration by matching relational structures. Pattern Recognition 17(1), 149–159 (1984)
Mokhtarian, F., Mackworth, K.: A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves. IEEE Transaction on Pami 14(8) (1992)
Niblack, W., Barber, R., Equitz, W., Flicker, M., Glasman, E., Petkovic, D., Yanker, P., Faloutsos, C.: The QBIC Project: Query images by content using color texture and shape. In: SPIE Proceedings of Storage and Retrieval for Image and Video Databases, pp. 173–187 (1993)
Scassellati, B., Alexopoulos, S., Flickner, M.D.: Retrieving images by-2D shape: a comparison of computation methods with human perceptual judgments. In: Proceeding of SPIE Storage and Retrieval for Image and Video databases, pp. 2–9 (1994)
Mehtre, B.M., Kankanhalli, M.S., Lee, W.F.: Shape measures for content based image retrieval: a comparison. Information Processing & Management 33(3), 319–337 (1997)
Jacobs, C.E., Finkelstein, A., Salesin, D.H.: Fast multiresolution image querying. In: Computer Graphics Proceeding SIGGRAPH 1995, pp. 277–286 (1995)
Jain, A.K., Vilaya, A.: Image retrieval using color and shape. Pattern Recognition 29(8), 1233–1244 (1996)
Kong, X.D., Luo, Q.S., Guo, Y., Zeng, G.H.: A New Boundary Descriptor Based on the Directional Distance Histogram. In: Proceedings of APCC 2006 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kong, X., Luo, Q., Zeng, G. (2007). A Novel SVM-Based Method for Moving Video Objects Recognition. In: Qiu, G., Leung, C., Xue, X., Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2007. Lecture Notes in Computer Science, vol 4781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76414-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-76414-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76413-7
Online ISBN: 978-3-540-76414-4
eBook Packages: Computer ScienceComputer Science (R0)