Multimodal learning for view-based 3D object classification

Nowadays by employing many machine learning and pattern classification methods in object classification, the view-based 3D object classification, an emerging research topic, becomes a major research focus. However, most existing researches focus on only a single modality of image features for the object classification, although recent studies have shown that different kinds of features may provide complementary information for 3D object classification. In this paper, we propose the multimodal support vector machine to combine three modalities of image features, i.e., Sift descriptor, Outline Fourier transform descriptor, and Zernike Moments descriptor to discriminate the multiple classes of object, where each kernel corresponds to each modality. In this way, not only the independence of each modality but also the interrelation between them are both taken into considered. And we further employ multi-task feature selection via the l2-norm regularization after feature extraction to improve the performance of final classification. The experiments conducted in ETH-80 image set demonstrate the effectiveness and superiority of our method.