Journal of Computer Science and Technology

, Volume 30, Issue 2, pp 340–352 | Cite as

RGB-D Hand-Held Object Recognition Based on Heterogeneous Feature Fusion

  • Xiong Lv
  • Shu-Qiang Jiang
  • Luis Herranz
  • Shuang Wang
Regular Paper

Abstract

Object recognition has many applications in human-machine interaction and multimedia retrieval. However, due to large intra-class variability and inter-class similarity, accurate recognition relying only on RGB data is still a big challenge. Recently, with the emergence of inexpensive RGB-D devices, this challenge can be better addressed by leveraging additional depth information. A very special yet important case of object recognition is hand-held object recognition, as manipulating objects with hands is common and intuitive in human-human and human-machine interactions. In this paper, we study this problem and introduce an effective framework to address it. This framework first detects and segments the hand-held object by exploiting skeleton information combined with depth information. In the object recognition stage, this work exploits heterogeneous features extracted from different modalities and fuses them to improve the recognition accuracy. In particular, we incorporate handcrafted and deep learned features and study several multi-step fusion variants. Experimental evaluations validate the effectiveness of the proposed method.

Keywords

RGB-D hand-held object recognition heterogeneous features fusion 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Xiong Lv
    • 1
  • Shu-Qiang Jiang
    • 1
  • Luis Herranz
    • 1
  • Shuang Wang
    • 1
  1. 1.Key Laboratory of Intelligent Information Processing of Chinese Academy of SciencesInstitute of Computing Technology Chinese Academy of SciencesBeijingChina

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