DocumentCode :
3492440
Title :
Normalized scoring of Hidden Markov Models by on-line learning and its application to gesture-sequence perception
Author :
Nishiyama, Mio ; Shibata, Tadashi
Author_Institution :
Dept. of Electr. Eng. & Inf. Syst., Univ. of Tokyo, Tokyo, Japan
fYear :
2009
fDate :
7-10 Nov. 2009
Firstpage :
3565
Lastpage :
3568
Abstract :
A normalized scoring algorithm has been developed for hidden Markov models (HMMs) to establish independent individual-model evaluation of each input sequence. Using this model, it has become possible for each trained HMM to judge if an input sequence is classified to the category of the model by a simple thresholding operation without referring to other models. Such evaluation has been enabled by creating a self model for each input sequence by on-line learning. As a result, a long action sequence composed of unit-motions can be recognized using multiple models each trained for each unit-motion. The algorithm was evaluated in 120 test sessions and the recognition rates of average 92.3% and 85.3% for unit motion detection and entire sequence recognition, respectively, have been demonstrated.
Keywords :
gesture recognition; hidden Markov models; image motion analysis; image sequences; learning (artificial intelligence); object detection; gesture sequence perception; hidden Markov models; independent individual model evaluation; motion analysis; motion recognition; normalized scoring algorithm; online learning; sequence recognition; unit motion detection; Hidden Markov models; Image processing; Information systems; Motion analysis; Motion detection; Pattern recognition; Robustness; Systems engineering and theory; Testing; Video sequences; Gesture perception; Hidden Markov Models; Image processing; Motion analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
ISSN :
1522-4880
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2009.5414331
Filename :
5414331
Link To Document :
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