Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval.
However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to
obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of
RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more
information. A very special and important case of object recognition is hand-held object recognition, as hand is
a straight and natural way for both human-human interaction and human-machine interaction. In this paper,
we study the problem of 3D object recognition by combining heterogenous features with different modalities
and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape
and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with
the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large
scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature.
In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in
RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human
skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using
linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld
objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.