Abstract
In temporal data analysis, noisy data is inevitable in both testing and training. This noise can seriously influence the performance of the temporal data analysis. To address this problem, we propose a novel method, termed Selective Temporal Filtering that builds a noise-free model for classification during training and identifies key-feature vectors that are noise-filtered data from the input sequence during testing. The use of these key-feature vectors makes the classifier robust to noise within the input space. The proposed method is validated on a synthetic-dataset and a database of American Sign Language. Using key-feature vectors results in robust performance with respect to the noise content. Futhermore, we are able to show that the proposed method not only outperforms Conditional Random Fields and Hidden Markov Models in noisy environments, but also in a well-controlled environment where we assume no significant noise vectors exist.
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Notes
1 American Sign Language Database, http://www.bu.edu/asllrp/ncslgr.html
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Acknowledgments
This work was partly supported by the ICT R&D program of MSIP/IITP [B0101-15-0552 , Development of Predictive Visual Intelligence Technology] and also supported by the Implementation of Technologies for Identification, Behavior, and Location of Human based on Sensor Network Fusion Program through the Ministry of Trade, Industry and Energy (Grant No. 10041629).
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Myung-cheol Roh is currently with S-1 Corporation.
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Roh, MC., Fazli, S. & Lee, SW. Selective temporal filtering and its application to hand gesture recognition. Appl Intell 45, 255–264 (2016). https://doi.org/10.1007/s10489-015-0757-8
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DOI: https://doi.org/10.1007/s10489-015-0757-8