An Attention Network Ensemble-based Study of Online in Air-writing Recognition
Aiming at the problems of small data samples,insufficient model generalization ability and low recognition rate of online In Air-Writing recognition,an ensemble-based online In Air-Writing recognition method was proposed.Firstly,the original multi-dimensional data was constructed by incorporating"online"time series features into the shape features,and the multi-dimensional fusion data was projected to three orthogonal planes to obtain three sets of projection features.Secondly,a convolutional neural network was constructed to extract the visual features,next character embedding was introduced as class labels of the image,and the class-labelled character-level semantic features were fused with the three sets of visual features through the attention detection mechanism to form three sets of semantically informative feature maps,and a SoftMax classifier was constructed based on the feature maps.Finally,the classification and recognition was performed by the main learner-based integrated voting method.Multiple sets of experiments were carried out on two sets of In Air-Writing datasets and the HIT-OR3C online dataset,and in the case of small sample recognition,the rec-ognition rates of the proposed method were better than that of other methods,which were 95.68%,93.02%and 94.96%respectively.The experimental result showed that the proposed method fully explored the effective features in the In Air-Writing data under the condition of small sample data,and improved the efficiency of In Air-Writing recognition.
In Air-WritingOn-Line Writingsmall sample learningdata fusionattention networkensemble learninggesture recogni-tion
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