RGB-D object recognition based on hybrid convolutional auto-encoder extreme learning machine

Learning rich representations efficiently plays an important role in RGB-D object recognition task, which is crucial to achieve high generalization performance. For the long training time of convolutional neural networks, a Hybrid Convolutional Auto-Encoder Extreme Learning Machine Structure (HCAE-ELM) was put forward which included Convolutional Neural Network (CNN) and Auto-Encoder Extreme Learning Machine (AE-ELM), which combined the power of CNN and fast training of AE-ELM. It used convolution layers and pooling layers to effectively abstract lower level features from RGB and depth images separately. And then, the shared layer was developed by combining these features from each modality and fed to an AE-ELM for higher level features. The final abstracted features were fed to an ELM classifier, which led to better generalization performance with faster learning speed. The performance of HCAE-ELM was evaluated on RGB-D object dataset. Experimental results show that the proposed method achieves better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.