Most discriminating segment - Longest common subsequence (MDSLCS) algorithm for dynamic hand gesture classification

Pattern Recognition Letters

Volumn 34, Issue 15, 2013, Pages 1980-1989

  Stern, Helman ^a   Shmueli, Merav ^a   Berman, Sigal ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Classification; Digits; Gesture recognition; Longest common subsequence

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; EXTRACTION; HIDDEN MARKOV MODELS; MARKOV PROCESSES;

AUTOMATIC EXTRACTION; DIGITS; DISCRIMINATIVE CLASSIFIERS; GESTURE TRAJECTORIES; HIDDEN MARKOV MODELS (HMMS); LONGEST COMMON SUBSEQUENCES; MOTION TRAJECTORIES; STATE-OF-THE-ART METHODS;

GESTURE RECOGNITION;

EID: 84885077333     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2013.02.007     Document Type: Article

References (32)

1. Alon, J., Athitsos, V., Sclaroff, S., 2005. Accurate and efficient gesture spotting via pruning and subgesture reasoning. In: IEEE ICCV Workshop on Human- Computer Interaction, pp. 189-198. (Pubitemid 43741582)
2. Bahlmann, C., Burkhardt, H., 2004. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans. Pattern Anal. Machine Intell. 26, 299-310.
3. Bergroth, L., Hakonen, H., Raita, T., 2000. A survey of longest common subsequence algorithms, string processing and information retrieval. In: 7th Internat. Symposium on String Process. Information Retrieval, SPIRE, pp. 39-48.
4. Buzan, D., Sclaroff, S., Kollios, G., 2004. Extraction and clustering of motion trajectories in video. In: Internat. Confer. on Pattern Recognition, Cambridge, UK, pp. 521-524.
5. Chen, Y., Nascimento, M.A., Ooi, B.C., Tung A.K.H., 2007. SpADe: on shape-based pattern detection in streaming time series. In: IEEE Internat. Confer. on Data Engineering, pp. 786-795. (Pubitemid 47422082)
6. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J., 2012. Deep big multilayer perceptrons for digit recognition. In: Montavon, G., Orr, G.B., Müller, K.-R. (Eds.), Neural Networks: Tricks of the Trade, Reloaded. Springer.
7. Corman, T.H., Leiserson, C.E., Riverst, R.L., Stein, C., 2001. Introduction to algorithms, 2nd ed. MIT Press, Cambridge, Massachusetts.
8. Corradini, A., 2001. Dynamic time warping for off-line recognition of a small gesture vocabulary. In: IEEE ICCV Workshop on Recognition, Anal. Tracking of Faces and Gestures in Real-Time Systems.
9. Frolova, D., Stern, H., Berman, S., in press. Most probable longest common subsequence for recognition of gesture character input, IEEE Trans. Systems Man Cybernet. B.
10. Hsu, W.-H., Chiang, Y.-Y., Wu, J.-S., 2010. Integrating weighted LCS and SVM for 3d handwriting recognition on handheld devices using accelerometers. WSEAS Trans. Computers 9 (3), 235-251.
11. Kasprzak, W., Wilkowski, A., Czapnik, K., 2012. Hand gesture recognition based on free-form contours and probabilistic inference. Internat. J. Appl. Math. Comput. Sci. 22 (2), 437-448.
12. Kuzmanic, A., Zanchi, V., 2007. Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system. In: Internat. Confer. on Computer as a Tool (EUROCON), pp. 264-269.
13. Liao, T.W., 2005. Clustering of time series data - a survey. Pattern Recognition 38 (11), 1857-1874.
14. Lichtenauer, J.F., Hendriks, E.A., Reinders, M.J.T., 2008. Sign language recognition by combining statistical DTW and independent classification. IEEE Trans. Pattern Anal. Machine Intell. 30 (11), 2040-2046.
15. Malgireddy, A., 2010. Framework for hand gesture recognition and spotting using sub-gesture modelling. In: Internat. Confer. on Pattern Recognition, pp. 3780-3783.
16. di Martino, J., 1985. Dynamic time warping algorithms for isolated and connected word recognition. In: Suen, C.Y. (Ed.), New Systems and Architectures for Automatic Speech Recognition and Synthesis. Springer-Verlag, New York, pp. 405-418.
17. Morris, B.T., Trivedi, M.M., 2009. Learning trajectory patterns by clustering: experimental studies and comparative evaluation. In: IEEE Confer. on Computer Vision and Pattern Recognition (CVPR '09).
18. Murakami, K., Taguchi, H., 1991. Gesture recognition using recurrent neural networks. In: SIGCHI Confer. on Human factors in comput. systems, pp. 237-242.
19. Oikonomopoulos, A., Pantic, M., 2007. Human body gesture recognition using adapted auxiliary particle filtering. In: IEEE Confer. on Advanced Video and Signal Based Surveillance (AVSS), pp. 441-446.
20. Piciarelli, C., Foresti, G., 2006. On-line trajectory clustering for anomalous events detection. Pattern Recognition Lett. 27 (15), 1835-1842. (Pubitemid 44353852)
21. Rautaray, S.S., Agrawal, A., 2012. Vision based hand gesture recognition for human computer interaction: a survey. Artificial Intelligence Review, 1-54.
22. Sankoff, D., Kruskall, J.B. (Eds.), 1983. Time warps, string edits, and macromolecules: theory and practice of sequence comparison. Addison-Wesley, Massachussetts.
23. Seewald, A.K., 2012. On the brittleness of handwritten digit recognition models. ISRN Machine Vision. Article ID 834127.
24. Siddharth, K.S., Jangid, M., Dhir, R., Rani, R., 2011. Handwritten gurmukhi character recognition using statistical and background directional distribution features. Internat. J. Computer Sci. Eng. (IJCSE) 3 (6), 2332-2345.
25. Stern, H., Frolova, D., Berman, S., 2010. Hand gesture recognition for TV remote control using tree-based ensemble and LCS classifiers. The World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP).
26. Tanaka, K., 2009. Gesture recognition with a focus on important actions by using a path searching method in weighted graph. Int. J. Computer Sci. Issues 6 (2), 14-19.
27. Thedoridis, S., Koutroumbas, K., 1999. Pattern Recognition, 4th ed. Academic Press, USA.
28. Wagner, R.A., Fischer, M.J., 1974. The string-to-string correction problem''. J. ACM 21 (1), 168-173.
29. Vlachos, M., Kollios, G., Gunopulos, G., 2002. Discovering similar multidimensional trajectories. In: Internat. Confer. on Data Engineering, pp. 673-684. (Pubitemid 34278779)
30. Yanagisawa, Y., Satoh, T., 2006. Clustering multidimensional trajectories based on shape and velocity. In: Internat. Confer. on Data Engineering Workshops (ICDEW).
31. Yang, S., Sarkar, R., 2006. Gesture recognition using hidden Markov models from fragmented observations. In: IEEE Confer. on Computer Vision and Pattern Recognition, pp. 766-773.
32. Zhu, Y., Ren, H., Xu, G., Lin, X., 2000. Toward real-time human-computer interaction with continuous dynamic hand gestures. In: IEEE Internat. Confer. on Automatic Face and Gesture Recognition.