RGB-D object recognition based on the joint deep random kernel convolution and ELM

Abstract

Nowadays RGB-D object recognition has been a challenging and important task in computer vision field. Convolutional Neural Network is a current popular algorithm for feature extraction from RGB and Depth modality separately, which cannot fully exploit some potential and complementary information between different modalities. The conventional training methods designed for CNN involve many gradient-descent searching, and usually face some troubles such as time-consuming convergence, local minima. In order to solve these problems, we propose a Joint Deep Radom Kernel Convolution and ELM (JDRKC-ELM) method for object recognition, which integrating the power of CNN feature extraction and fast training of ELM-AE. Our JDRKC-ELM can learn feature representations from raw RGB-D data directly. In this structure, Radom Kernel Convolutional neural network (RKCNN) is used for lower-level feature extraction from RGB and Depth modality separately. And then, combining these features from different modality by a feature fusion layer and feeding these fusion features to a Double-layer ELM-AE (DLELM-AE) for higher-level features. At last, the final feature representations are sent to a standard ELM for the object classification. We evaluate the quality of the JDRKC-ELM method on the RGB-D Object Dataset. The results show that the proposed method achieves high recognition accuracy and good generalization performance in comparison with deep learning methods and other ELM methods.