期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2019
卷号：29
期号：3
页码：1-11
DOI：10.2478/amcs-2019-0045
出版社：De Gruyter Open
摘要：With the advent of 3D cameras, getting depth information along with RGB images has been facilitated, which is helpful
in various computer vision tasks. However, there are two challenges in using these RGB-D images to help recognize
RGB images captured by conventional cameras: one is that the depth images are missing at the testing stage, the other
is that the training and test data are drawn from different distributions as they are captured using different equipment. To
jointly address the two challenges, we propose an asymmetrical transfer learning framework, wherein three classifiers are
trained using the RGB and depth images in the source domain and RGB images in the target domain with a structural risk
minimization criterion and regularization theory. A cross-modality co-regularizer is used to restrict the two-source classifier
in a consistent manner to increase accuracy. Moreover, an L2,1 norm cross-domain co-regularizer is used to magnify
significant visual features and inhibit insignificant ones in the weight vectors of the two RGB classifiers. Thus, using the
cross-modality and cross-domain co-regularizer, the knowledge of RGB-D images in the source domain is transferred to
the target domain to improve the target classifier. The results of the experiment show that the proposed method is one of
the most effective ones.
关键词：object recognition; RGB;D images; transfer learning; privileged information;
