Recognition of Signed Expressions Using Cluster-Based Segmentation of Time Series
The paper considers automatic visual recognition of signed expressions. The proposed method is based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. To define the subunits a data-driven procedure is applied. The procedure consists in partitioning time series, extracted from video, into subsequences which form homogeneous groups. The cut points are determined by an evolutionary optimization procedure based on multicriteria quality assessment of the resulting clusters. In the paper the problem is formulated, its solution method is proposed and experimentally verified on a database of 100 Polish words.
KeywordsHide Markov Model Dynamic Time Warping Multiobjective Optimization Problem Polish Sign Language Cluster Validity Index
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