Combining Features For RGB-D object Recognition

Object category and instance recognition have received much attention in this era of modern technologies. Advanced image sensing technologies provide high resolution color and depth synchronized videos such as RGB-D (Kinect style) camera. At present, various features extraction schemes are introduced to improve classification performance. Extracting useful features from both color and depth images have recently gained much attention in this research area. In this paper, we proposed an approach to create new features using a feature combination of various feature extraction techniques: color autrocorrelogram, wavelet moments, local binary pattern (LBP) and principal component analysis (PCA) for RGB-D data. The experiments on benchmark dataset shows that the new features obtained by the proposed method using k-nearest neighbor (k-NN) classifier provide promising classification results.