Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Learning Models for Semantic Classification of Insufficient Plantar Pressure Images

Blog Article

Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set.This paper has been inspired with insufficient data-set learning algorithms, such Gas Oven Valve as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method.Firstly, two basic models for transfer learning are introduced.A classification system and calculation criteria are then subsequently introduced.Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.

5%.Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN.Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation).The proposed method SUPER MULTI-WITH IRON for the plantar pressure classification task shows high performance in most indices when comparing with other methods.The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields.