Evolutionary Feature Learning for 3-D Object Recognition

3-D object recognition is a challenging task for many applications including autonomous robot navigation and scene understanding. Accurate recognition relies on the selection/learning of discriminative features that are in turn used to uniquely characterize the objects. This paper proposes a novel evolutionary feature learning (EFL) technique for 3-D object recognition. The proposed novel automatic feature learning approach can operate directly on 3-D raw data, alleviating the need for data pre-processing, human expertise and/or defining a large set of parameters. EFL offers smart search strategy to learn the best features in a huge feature space to achieve superior recognition performance. The proposed technique has been extensively evaluated for the task of 3-D object recognition on four popular data sets including Washington RGB-D (low resolution 3-D Video), CIN 2D3D, Willow 2D3D and ETH-80 object data set. Reported experimental results and evaluation against existing state-of-the-art methods (e.g., unsupervised dictionary learning and deep networks) show that the proposed EFL consistently achieves superior performance on all these data sets.