Partially Common-Semantic Pursuit for RGB-D Object Recognition
Lu Jin, Zechao Li, Xiangbo Shu, Shenghua Gao, Jinhui Tang · 2015
For the RGB-D object recognition task, the robust and rich representations can boost the performance. Most works employ feature learning approaches to learn specific representation for the RGB and depth modalities independently, while some directly learn common property. Different from them, this paper proposes a novel supervised feature learning method for RGB-D object recognition, named Partially Common-Semantic Learning (PCSL), which jointly captures the complementary and consistency semantic information from RGB and depth modalities. The complementary information is revealed by the individual modality, while the consistency is exploited by both modalities simultaneously. In PCSL, Reconstruction Independent Component Analysis (RICA) is extended to integrate the supervised information and learn both of the complementary and partially shared common semantic information. The proposed approach is evaluated on two public RGB-D datasets and achieves better performance than several state-of-the-art methods.