Hand-Crafted Features or Machine Learnt Features? Together They Improve RGB-D Object Recognition

Abstract

RGB-D object recognition is an important research topic in computer version, and seeking a robust image representation is the most important sub problem for RGB-D object recognition. On the one hand, the recently emerging deep learning methods, which learns image representations automatically by capturing the data structure, have demonstrated the impressive performance for object recognition. On the other hand, the previously commonly used hand-crafted features also encodes the prior knowledge about the data. By realizing that the hand-crafted features and machine learnt features actually characterize the different aspects of image data, rather than only using one type of feature, we propose to jointly use the machine learnt features and hand-crafted features for RGB-D object recognition. Specifically, we use the Convolution Neural Networks (CNNs) to extract the machine learnt representation, and use Locality-constrained Linear Coding (LLC) based spatial pyramid matching for hand-crafted features. We evaluated our proposed approach on three publicly available RGB-D datasets. Experimental results show that our method achieves the best performance under all the cases, which demonstrates the effectiveness of our method.