RGB-D object recognition based on RGBD-PCANet learning
In this paper, a simple deep learning method namely RGBD-PCANet is proposed for object recognition effectively. The proposed method extends the original PCANet for RGB-D images. Firstly, the RGB and depth images are preprocessed to meet the requirement of the network input layer. Secondly, features of RGB-D images are extracted by the two stages RGBD-PCANet which consists of cascaded PCA, binary hashing, and block-wise histograms. Finally, the SVM method is used as classifier. We evaluate the proposed method on the popular Washington RGB-D Object dataset. Extensive experiments demonstrate that the proposed RGBD-PCANet method achieves comparable performance to state-of-the-art CNN-based methods and the runtimes are low without GPU acceleration.