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

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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.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology Chinese Academy of Sciences, Beijing, 100190, China

    Xiong Lv, Shu-Qiang Jiang, Luis Herranz & Shuang Wang

Authors
  1. Xiong Lv
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  2. Shu-Qiang Jiang
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  3. Luis Herranz
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  4. Shuang Wang
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Corresponding author

Correspondence to Shu-Qiang Jiang.

Additional information

Special Section on Object Recognition

This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2012CB316400, the National Natural Science Foundation of China under Grant Nos. 61322212 and 61450110446, the National High Technology Research and Development 863 Program of China under Grant No. 2014AA015202, and the Chinese Academy of Sciences Fellowships for Young International Scientists under Grant No. 2011Y1GB05. This work is also funded by Lenovo Outstanding Young Scientists Program (LOYS).

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Lv, X., Jiang, SQ., Herranz, L. et al. RGB-D Hand-Held Object Recognition Based on Heterogeneous Feature Fusion. J. Comput. Sci. Technol. 30, 340–352 (2015). https://doi.org/10.1007/s11390-015-1527-0

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  • DOI: https://doi.org/10.1007/s11390-015-1527-0

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