Learning Coupled Classifiers with RGB images for RGB-D object recognition

Abstract

The emergence of depth images opens a new dimension to address the challenging object recognition tasks. However, when only a small amount of labeled data is available, we cannot learn a discriminative classifier directly using the RGB-D images. To cope with this problem, we proposed a new method, Learning Coupled Classifiers with RGB images for RGB-D object recognition (LCCRRD). We learn the coupled classifiers using RGB images from source domain, the combined RGB and depth images from target domain and RGB images from target domain. The predicted results of the two target classifiers are made to be similar to make them more accurate. We also utilize the correlation between source and target RGB images to boost the relevant features and eliminate the irrelevant features. It also has the capacity to incorporate the manifold structure into our model. Furthermore, a unified objective function is presented to learn the classifier parameters. To evaluate our LCCRRD method, we apply it to five cross domain datasets. The experimental results demonstrate that our method can achieve competing performance against the state-of-art methods for object recognition tasks.