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Towards Space-Time Semantics in Two Frames

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 7585))

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Abstract

We present a novel, low-level scheme to analyze spatial and temporal change within a local support region. Assuming available region correspondences between two adjacent frames, we divide each region into a regular grid of patches. Depending on the change of an image function inside the patch over time, each patch is assigned weights for the following four labels: “C” for a constant patch, “O” when new information originates from outside the support region, “I” for “inner” changes, and “N” for information from neighboring patches. Our method goes beyond optical flow, as it provides an additional semantic level of understanding the changes in space-time. We demonstrate how our novel “COIN” scheme can be used to categorize local space-time events in image pairs, including locally planar support regions, 3D discontinuities, and virtual vs. real crossings of 3D structures.

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References

  1. Doretto, G., Chiuso, A., Wu, Y., Soatto, S.: Dynamic textures. Int. J. of Comp. Vis. 51, 91–109 (2003)

    Article MATH  Google Scholar 

  2. Derpanis, K., Wildes, R.: Spacetime texture representation and recognition based on a spatiotemporal orientation analysis. IEEE TPAMI (2012)

    Google Scholar 

  3. Doulamis, A., Doulamis, N., Ntalianis, K., Kollias, S.: An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture. Trans. Neur. Netw. 14, 616–630 (2003)

    Article  Google Scholar 

  4. Ogale, A.S., Fermüller, C., Aloimonos, Y.: Motion segmentation using occlusions. IEEE Trans. Pattern Anal. Mach. Intell. 27, 988–992 (2005)

    Article  Google Scholar 

  5. Stein, A.N., Hebert, M.: Local detection of occlusion boundaries in video. Image Vision Comput. 27, 514–522 (2009)

    Article  Google Scholar 

  6. Derpanis, K.G., Wildes, R.P.: Detecting Spatiotemporal Structure Boundaries: Beyond Motion Discontinuities. In: Zha, H., Taniguchi, R.-I., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp. 301–312. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS (2005)

    Google Scholar 

  8. Ke, Y., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: Proc. ICCV, vol. 1, pp. 166–173 (2005)

    Google Scholar 

  9. Laptev, I., Lindeberg, T.: Space-time interest points. In: Proc. ICCV (2003)

    Google Scholar 

  10. Laptev, I.: On space-time interest points. Int. J. of Comp. Vis. 64, 107–123 (2005)

    Article  Google Scholar 

  11. Le, Q.V., Zou, W.Y., Yeung, S.Y., Ng, A.Y.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: Proc. CVPR, pp. 3361–3368 (2011)

    Google Scholar 

  12. Wang, H., Ullah, M.M., Kläser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: Proc. BMVC, p. 127 (2009)

    Google Scholar 

  13. Kläser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3d-gradients. In: Proc. BMVC, pp. 995–1004 (2008)

    Google Scholar 

  14. Brkić, K., Pinz, A., Šegvić, S., Kalafatić, Z.: Histogram-Based Description of Local Space-Time Appearance. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 206–217. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  15. Aanæs, H., Dahl, A., Steenstrup Pedersen, K.: Interesting interest points. Int. J. of Comp. Vis. 97, 18–35 (2012)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia

    Karla Brkić, Zoran Kalafatić & Siniša Šegvić

  2. Graz University of Technology, Austria

    Axel Pinz

Authors
  1. Karla Brkić

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  2. Axel Pinz

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  3. Zoran Kalafatić

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  4. Siniša Šegvić

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Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria Elettrica, Gestionale e Meccanica (DIEGM), Università degli Studi di Udine, Via delle Scienze, 208, 33100, Udine, Italy

    Andrea Fusiello

  2. IIT Istituto Italiano di Tecnologia, Via Morego 30, 16163, Genoa, Italy

    Vittorio Murino

  3. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Modena e Reggio Emilia, Strada Vignolege, 905, 41125, Modena, Italy

    Rita Cucchiara

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© 2012 Springer-Verlag Berlin Heidelberg

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Brkić, K., Pinz, A., Kalafatić, Z., Šegvić, S. (2012). Towards Space-Time Semantics in Two Frames. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33885-4_13

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