用于对象识别的多模态局部接受野极限学习机

Fengxue Li, Huaping Liu, Xinying Xu, F. Sun
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引用次数: 41

摘要

在多模态识别任务中,高效地学习丰富的表征是实现高泛化性能的关键。为了解决这一问题,本文提出了一种有效的多模态局部接受野极限学习机(MM-ELM-LRF)结构,同时保持了ELM训练效率的优势。在该结构中,首先对每个模态分别进行ELM-LRF特征提取。然后,将每个模态的这些特征结合起来开发共享层。最后,使用极限学习机(ELM)作为监督特征分类器进行最终决策。在华盛顿RGB-D目标数据集上的实验验证表明,多模态融合方法具有较好的识别性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Modal Local Receptive Field Extreme Learning Machine for object recognition
Learning rich representations efficiently plays an important role in multi-modal recognition task, which is crucial to achieve high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Local Receptive Field Extreme Learning Machine (MM-ELM-LRF) structure, while maintaining ELM's advantages of training efficiency. In this structure, ELM-LRF is firstly conducted for feature extraction for each modality separately. And then, the shared layer is developed by combining these features from each modality. Finally, the Extreme Learning Machine (ELM) is used as supervised feature classifier for the final decision. Experimental validation on Washington RGB-D Object Dataset illustrates that the proposed multiple modality fusion method achieves better recognition performance.
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