Classifier Comparison for Repeating Motion Based Video Classification

1. Ayyildiz, K., Conrad, S.: Video classification by main frequencies of repeating movements. In: Int. Workshop on Image Analysis for Multimedia Interact. Serv. (2011)

   Google Scholar 

2. Wong, W., Siu, W., Lam, K.: Generation of moment invariants and their uses for character recognition. Pattern Recognition Letters, 115–123 (1995)

   Google Scholar 

3. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 21–27 (1967)

   Google Scholar 

4. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval. Inf. Processing and Management, 465–476 (1986)

   Google Scholar 

5. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Inf. Retrieval (2008)

   Google Scholar 

6. Hill, T., Lewicki, P.: Statistics: Methods and Applications (2006)

   Google Scholar 

7. Foody, G.M.: Relating the land-cover composition of mixed pixels to ann classification output. Photogrammetric Engineering and Remote Sensing, 491–499 (1996)

   Google Scholar 

8. LLC, Y.: Youtube: Broadcast yourself, youtube.com (2013)

9. Neuroph: Java neural network framework, neuroph.sourceforge.net (2013)

10. Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines, csie.ntu.edu.tw/~cjlin/libsvm (2013)

11. Pei, S., Chen, F.: Semantic scenes detection and classification in sports videos. In: Conference on Computer Vision, Graphics and Image Processing, pp. 210–217 (2003)

    Google Scholar 

12. Lienhart, R.: Indexing and retrieval of digital video sequences based on automatic text recognition. In: 4th ACM International Conference on Multimedia, pp. 419–420 (1996)

    Google Scholar 

13. Patel, N., Sethi, I.: Audio characterization for video indexing. In: SPIE on Storage and Retrieval for Still Image and Video Databases, pp. 373–384 (1996)

    Google Scholar 

14. Meng, Q., Li, B., Holstein, H.: Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach. Image and Vision Computing, 795–809 (2006)

    Google Scholar 

15. Cheng, F., Christmas, W., Kittler, J.: Periodic human motion description for sports video databases. In: Int. Conference on Pattern Recognition, pp. 870–873 (2004)

    Google Scholar 

16. Sobral, A., Oliveira, L., Schnitman, L., Souza, F.D.: Highway traffic congestion classification using holistic properties. In: 10th IASTED International Conference on Signal Processing, Pattern Recognition and Applications (2013)

    Google Scholar 

17. Saeedi, S., Moussa, A., El Sheimy, N.: Vision-aided context-aware framework for personal navigation services. International Society for Photogrammetry and Remote Sensing, 231–236 (2012)

    Google Scholar 

18. Glette, K., Gruber, T., Kaufmann, P., Torresen, J., Sick, B., Platzner, M.: Comparing evolvable hardware to conventional classifiers for electromyographic prosthetic hand control. In: NASA/ESA Conf. on Adaptive Hardware and Systems, pp. 32–39 (2008)

    Google Scholar 

19. Rocamora, M., Herrera, P.: Comparing audio descriptors for singing voice detection in music audio files. In: 11th Brazilian Symposium on Computer Music (2007)

    Google Scholar 

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