拡張Jaccardグラフと確率的勾配降下法による特徴融合を用いたロボット認識
Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot
KinectのRGBと深度情報を統合する特徴グラフ融合(FGF)を提案し、複数Jaccard類似度と単語埋め込みで顔・物体認識の精度を高めた。
著者: Shenglan Liu, Muxin Sun, Wei Wang, Feilong Wang
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
