02230nas a2200277   4500000000100000000000100001008004100002260001200043653002100055653003100076653001300107653002100120653002300141653002500164653001000189100001700199700001100216700001100227700001900238245008800257856009900345300001000444490000600454520147800460022001401938       2020                            d  c03/202010aImage Processing10aArtificial Neural Networks10aAnalysis10aMachine Learning10aFeature Extraction10aImage Classification10aImage1 aNilanjan Dey1 aYao Wu1 aQun Wu1 aSimon Sherratt00aLearning Models for Semantic Classification of Insufficient Plantar Pressure Images  uhttps://www.ijimai.org/journal/sites/default/files/files/2020/03/ijimai20206_1_6_pdf_49479.pdf  a51-610 v63 aEstablishing 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 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 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.  a1989-1660