Multi-view Object Recognition Based on Deep Models

Object classification and retrieval play important roles in many computer vision tasks. To achieve a good performance on RGB images, we propose a deep learning based multi-view method for both classification and retrieval. Different information of a static object is represented in multiple views, which makes the features extracted more stable against view variance and complex environment. To take advantage of both excellent performance of CNN architectures and rich information in multiple views, our approach is based on the Inception with Batch Normalization network for feature extraction and a post-fusion method is adopted. To reduce the effect of noise and improve computation efficiency, a denoising technology is explored to simulate infinite times corruption on the features for robust representation. The proposed method is capable of achieving high performance with a handful of training views, even better than some algorithms based on both RGB and depth modalities. The performance of the method is proved to be stable with different numbers of views as testing, and is comparable even with one testing view. Besides, the method is evaluated across datasets together with a popular Multi-view deep learning algorithms (MVCNN) and shows favourably influence can be brought by datasets transfer. This method is implemented on two kinds of benchmarks which are distinct in sizes to verify the efficiency and robustness of our method.