Reduced Biquaternion Stacked Denoising Convolutional AutoEncoder for RGB-D Image Classification

RGB-D image classification based on convolutional neural networks have been extensively explored recently. However, they suffer from problems of effective representation of RGB-D image, intra-class variances and inter-class similarities. To address these problems, this letter proposes a novel RGB-D image classification framework based on reduced biquaternion stacked denoising convolutional autoencoder (RQ-SDCAE). The proposed framework can encode and extract the depth feature effectively by using the reduced biquaternion. The stacked training method is utilized to train the proposed reduced biquaternion convolutional autoencoder. Extensive evaluations for RGB-D image classification demonstrate that RQ-SDCAE outperforms the state-of-the-art methods.