Abstract
In applications of behavior recognition, the use of spatiotemporal invariant feature points can improve the robustness to noise, illumination and geometric distortions. In this paper, we develop a novel detection model of spatiotemporal invariant feature by generalizing the notion of image phase congruency to video volume phase congruency. The proposed model detects feature points by measuring the spatiotemporal phase congruency of Fourier series components along with their characteristic scale and principal orientation. Compared with other state-of-the-art methods, the key advantages of this interest point detector include the invariance to contrast variations and more precise feature location. Furthermore, an invariant feature descriptor is advanced based on the phase congruency map, resulting in enhanced discriminative power in classification tasks. Experimental results on KTH human motion dataset demonstrate the validity and effectiveness of the extracted invariant features in the human behavior recognition scheme.
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References
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio-Temporal Features. In: ICCV VS-PETS 2005, Beijing, China (2005)
Haralick, R., Shapiro, L.: Computer and Robot Vision II. Addison-Wesley, Reading (1993)
Laptev, I.: On Space-Time Interest Points. International Journal of Computer Vision 64(2/3), 107–123 (2005)
Harris, C., Stephens: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–152 (1988)
Oikonomopoulos, A., Patras, I., Pantic, M.: Spatiotemporal saliency for human action recognition. In: ICME 2005, pp. 430–433 (2005)
Kadir, T., Brady, M.: Scale saliency: a novel approach to salient feature and scale selection. In: International Conference on Visual Information Engineering, November 2000, pp. 25–28 (2000)
Kovesi, P.D.: A Dimensionless Measure of Edge Significance from Phase Congruency Calculated via Wavelets. In: The First New Zealand Conference on Image and Vision Computing, Auckland, August 16-18, pp. 87–94 (1993)
Kovesi, P.: Image Features From Phase Congruency. Videre: A Journal of Computer Vision Research 1(3) (Summer 1999)
Kovesi, P.: Phase Congruency Detects Corners and Edges. In: The Australian Pattern Recognition Society Conference: DICTA 2003, Sydney, December 2003, pp. 309–318 (2003)
Oppenheim, A.V., Lim, J.S.: The importance of phase in signal. Proceedings of the IEEE 69, 529–541 (1981)
Morrone, M.C., Ross, J.R., Burr, D.C., Owens, R.A.: Mach bands are phase dependent. Nature 324(6094), 250–253 (1986)
Morrone, M.C., Owens, R.A.: Feature detection from local energy. Pattern Recognition Letters 6, 303–313 (1987)
Venkatesh, S., Owens, R.A.: An energy feature detection scheme. In: The International Conference on Image Processing, Singapore, pp. 553–557 (1989)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. In: ICPR, pp. 32–36 (2004)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Myerscough, P.J., Nixon, M.S.: Temporal phase congruency. In: The 6th. IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 76–79 (2004)
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Wang, H., Xu, Y., Yang, X., Song, L., Zhang, W. (2009). Spatiotemporal Phase Congruency Based Invariant Features for Human Behavior Classification. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_8
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DOI: https://doi.org/10.1007/978-3-642-10467-1_8
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