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

Abstract · Mar 24, 2017 11:58 ·

cs-cv cs-lg cs-ro

Arxiv Abstract

  • Shenglan Liu
  • Muxin Sun
  • Wei Wang
  • Feilong Wang

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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