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Multi-channel feature dictionaries for RGB-D object recognition
Author(s): Xiaodong Lan; Qiming Li; Mina Chong; Jian Song; Jun Li
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Paper Abstract

Hierarchical matching pursuit (HMP) is a popular feature learning method for RGB-D object recognition. However, the feature representation with only one dictionary for RGB channels in HMP does not capture sufficient visual information. In this paper, we propose multi-channel feature dictionaries based feature learning method for RGB-D object recognition. The process of feature extraction in the proposed method consists of two layers. The K-SVD algorithm is used to learn dictionaries in sparse coding of these two layers. In the first-layer, we obtain features by performing max pooling on sparse codes of pixels in a cell. And the obtained features of cells in a patch are concatenated to generate patch jointly features. Then, patch jointly features in the first-layer are used to learn the dictionary and sparse codes in the second-layer. Finally, spatial pyramid pooling can be applied to the patch jointly features of any layer to generate the final object features in our method. Experimental results show that our method with first or second-layer features can obtain a comparable or better performance than some published state-of-the-art methods.

Paper Details

Date Published: 10 April 2018
PDF: 7 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061515 (10 April 2018); doi: 10.1117/12.2303479
Show Author Affiliations
Xiaodong Lan, Quanzhou Institute of Equipment Manufacturing Haixi Institutes (China)
Qiming Li, Quanzhou Institute of Equipment Manufacturing Haixi Institutes (China)
Mina Chong, Quanzhou Institute of Equipment Manufacturing Haixi Institutes (China)
Jian Song, Quanzhou Institute of Equipment Manufacturing Haixi Institutes (China)
Jun Li, Quanzhou Institute of Equipment Manufacturing Haixi Institutes (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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