Feature fusion using Extended Jaccard Graph and word embedding for robot

Abstract

PurposeRobot vision is a fundamental device for human–robot interaction and robot complex tasks. In this paper, the authors aim to use Kinect and propose a feature graph fusion (FGF) for robot recognition.Design/methodology/approachThe feature fusion utilizes red green blue (RGB) and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and word embedding method to enhance the recognition results.FindingsThe authors also collect DUT RGB-Depth (RGB-D) face data set and a benchmark data set to evaluate the effectiveness and efficiency of this method. The experimental results illustrate that FGF is robust and effective to face and object data sets in robot applications.Originality/valueThe authors first utilize Jaccard similarity to construct a graph of RGB and depth images, which indicates the similarity of pair-wise images. Then, fusion feature of RGB and depth images can be computed by the Extended Jaccard Graph using word embedding method. The FGF can get better performance and efficiency in RGB-D sensor for robots.