Multi-channel feature dictionaries for RGB-D object recognition

Xiaodong Lan; Qiming Li Jr.; Mina Chong Jr.; Jian Song; Jun Li

doi:10.1117/12.2303479

10 April 2018 Multi-channel feature dictionaries for RGB-D object recognition

Xiaodong Lan, Qiming Li Jr., Mina Chong Jr., Jian Song, Jun Li

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061515 (2018) https://doi.org/10.1117/12.2303479
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

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.

Citation Download Citation

Xiaodong Lan, Qiming Li Jr., Mina Chong Jr., Jian Song, and Jun Li "Multi-channel feature dictionaries for RGB-D object recognition", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061515 (10 April 2018); https://doi.org/10.1117/12.2303479

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