GPU Hierarchical Quilted Self Organizing Maps for Multimedia Understanding

Y. Nashed
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引用次数: 1

Abstract

It is well established that the human brain outperforms current computers, concerning pattern recognition tasks, through the collaborative processing of simple building units (neurons). In this work we expand an abstracted model of the neocortex called Hierarchical Quilted Self Organizing Map, benefiting from the parallel power of current Graphical Processing Units, to achieve realtime understanding and classification of spatio-temporal sensory information. We also propose an improvement on the original model that allows the learning rate to be automatically adapted according to the input training data available. The overall system is tested on the task of gesture recognition from a Microsoft Kinect publicly available dataset.
面向多媒体理解的GPU分层绗缝自组织地图
众所周知,在模式识别任务方面,人脑通过对简单构建单元(神经元)的协同处理,胜过当前的计算机。在这项工作中,我们扩展了一个被称为分层绗绗自组织地图的新皮层抽象模型,利用当前图形处理单元的并行能力,实现对时空感官信息的实时理解和分类。我们还提出了对原始模型的改进,允许学习率根据可用的输入训练数据自动调整。整个系统在微软Kinect公开数据集的手势识别任务上进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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