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Action Classification Based on Mutual Difference Score

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Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 515))

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Abstract

Human action recognition refers to the classification of human action from video clips automatically. Images extracted from the video clips at regular time interval are processed to identify the action contained in them. This is done by comparing these images with images taken from appropriate standard action databases. Thus, human action recognition becomes the task of verifying the similarity between two images. This paper proposes mutual difference score as a measure of similarity between two images. The proposed measure has been validated using the Weizmann and KTH datasets.

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References

  1. Bhateja, V., Malhotra, C., Rastogi, K. Verma, A.: Improved Decision Median Filter for Video Sequences Corrupted by Impulse Noise, Int. Conference on Signal Processing and Integrated Networks, 716–721 (2014)

    Google Scholar 

  2. Finlayson, G. D., Drew, M. S., Lu, C.: Intrinsic Images by Entropy Minimization. 8th European Conference on Computer Vision, LNCS, 582–595 (2004)

    Google Scholar 

  3. Fish, B., Khan, A., Chehade, N. H., Chien, C., Pottie, G.: Feature Selection based on Mutual Information for Human Activity Recognition, IEEE International Conference on Acoustics, Speech and Signal Processing, 1729–1732 (2012)

    Google Scholar 

  4. Gorelick, L., Blank, M., Shechtam, E., Irani, M., Basri, R.: Actions as Space - Time Shapes. Tenth IEEE International Conference on Computer Vision - ICCV (2005)

    Google Scholar 

  5. Guisado, J. L., Jimenez-Morales, F., Guerra, J. M.: Application of Shannon’s entropy to Classify Emergent Behaviors in a Simulation of Laser Dynamics. Computational Methods in Sciences and Engineering, 213–216 (2003)

    Google Scholar 

  6. Huang, Q. M., Tong, X. J., Zeng, S., Wang, W. K.: Digital Image Resolution and Entropy. IEEE International Conference on Machine Learning and Cybernetics, 3, 1574–1577 (2007)

    Google Scholar 

  7. Kapur, J. N., Sahoo, P. K., Wong, A. K.: New Method for Gray-Level Picture Thresholding using Entropy of the Histogram. Computer Vision, Graphics and Image Processing, 29, 273–285 (1985)

    Google Scholar 

  8. Marvizadeh, S. Z., Choobineh, F. F.: Entropy based Dispatching for Automatic Guided Vehicles. International Journal of Production Research, 52(11), 3303–3316 (2014)

    Google Scholar 

  9. Mistry, D., Banerjee, A., Tatu, A.: Image Similarity based on Joint Entropy (Joint Histogram). International Conference on Advances in Engineering and Technology (2013)

    Google Scholar 

  10. Pal, N. R., Pal, S. K.: Entropy: A New Definition and its Application. IEEE Transactions on Systems, Man and Cybernetics, 21(5), 1260–1270 (1991)

    Google Scholar 

  11. Rodriguez, M. D., Ahmed, J., Shah, M.: Action MACH: A Spatio-temporal Maximum Average Correlation Height Filter for Action Recognition. Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  12. Russakoff, D. B., Tomsai, C., Rohlfing, T., Maurer Jr., C. R.: Image Similarity Using Mutual Information of Regions. 8th European Conf. on Computer Vision, 596–607 (2004)

    Google Scholar 

  13. Shannon, C. E.: A Mathematical Theory of Communication. The Bell System Technical Journal, 27, 379–423 (1948)

    Google Scholar 

  14. Soomro, K., Zamir, R. A.: Action Recognition in Realistic Sports Videos. In Computer Vision in Sports. Springer International Publishing (2014).

    Google Scholar 

  15. Thum, C.: Measurement of the Entropy of an Image with Application to Image Focusing. Optica Acta: International Journal of Optics, 31(2) (1984)

    Google Scholar 

  16. Yanai, K., Barnard, K.: Image Region Entropy: A Measure of Visualness of Web Images Associated with One Concept. 13th ACM Int. Conference on Multimedia, 419–422 (2005)

    Google Scholar 

  17. Yao, B., Khosla, A., Li, F. F.: Classifying Actions and Measuring Action Similarity by Modeling the Mutual Context of Objects and Human Poses. 28th International Conference on Machine Learning, Bellevue, USA (2011)

    Google Scholar 

  18. Yuan, J., Liu, Z., Wu, Y.: Discriminative Subvolume Search for Efficient Action Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2442–2449 (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, India

    Shamama Anwar

  2. Department of Manufacturing Engineering, National Institute of Foundry & Forge Technology, Ranchi, India

    G. Rajamohan

Authors
  1. Shamama Anwar
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  2. G. Rajamohan
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Corresponding author

Correspondence to G. Rajamohan .

Editor information

Editors and Affiliations

  1. ANITS, Prof., Comp. Sci. & Engg. Dept. ANITS, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Dept. of ECE, Shri Ramswaroop Mem. Group of Prof. Clg Dept. of ECE, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. SCIS, University of Hyderabad , Hyderabad, India

    Siba K. Udgata

  4. KIIT University, School of Computer Engineering KIIT University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

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Anwar, S., Rajamohan, G. (2017). Action Classification Based on Mutual Difference Score. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_71

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  • DOI: https://doi.org/10.1007/978-981-10-3153-3_71

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3152-6

  • Online ISBN: 978-981-10-3153-3

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