基于多模态的在线序列极限学习机研究

单一模态包含的物体信息有限,导致在物体材质识别分类中表现不佳,而传统多模态融合方法在样本训练过程中需要输入所有数据.提出一种多模态的多尺度局部感受野在线序列极限学习机方法.对物体不同模态样本运用改进的特征提取框架,利用多尺度局部感受野感知样本信息提取特征,并将不同模态特征融合后通过在线序列极限学习机进行训练学习.在线序...

Research on Online Sequence Extreme Learning Machine Based on Multi-Modal

The object information that a single modality contains is limited,degrading the performance in object material recognition and classification.At the same time,the sample training of the traditional multi-modal fusion methods require all data to participate.To address the problem,a multi-modal online sequence extreme learning machine method with multi-scale Local Receptive Fields(LRF) is proposed.The method employs an improved feature to extract the framework of different modality samples of the objects,and then uses multi-scale local receptive fields to perceive sample information and extract the features.Different modality features are fused through the Online Sequence Extreme Learning Machine(OSELM) for training and learning.The online sequence extreme learning machine can be trained with incrementally input samples during the training process,and does not need to retrain all the data every time there is new data to be trained.The method is verified on the TUM tactile texture database.The experimental results show that the classification accuracy of fused multi-modal is higher than that of the single modality,and the improved feature extraction framework can significantly improve the classification performance.