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arXiv · 1703.08378

Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot

Shenglan Liu·Muxin Sun·Wei Wang·Feilong Wang
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Abstract

Robot vision is a fundamental device for human-robot interaction and robot complex tasks. In this paper, we use Kinect and propose a feature graph fusion (FGF) for robot recognition. Our feature fusion utilizes RGB and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and utilize word embedding method to enhance the recognition results. We also collect DUT RGB-D face dataset and a benchmark datset to evaluate the effectiveness and efficiency of our method. The experimental results illustrate FGF is robust and effective to face and object datasets in robot applications.

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Shenglan Liu, Muxin Sun, Wei Wang, Feilong Wang. 2017-03-24. Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot. https://arxiv.org/abs/1703.08378

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