关键词: 模型标注, 测度学习, 相关成分分析

Abstract: In order to improve the 3D model annotation performance using a large number of unlabeled samples, this paper proposes a semi-supervised measure learning method to realize the 3D models multiple semantic annotation. A graph-based semi-supervised learning is firstly used to expand the training set, and the semantic words confidence of the models in the extension set is proposed. An improved relevant component analysis method is introduced in this paper to learn a distance measure based on the extended training set. The approach is introduced to complete multiple semantic annotation task based on the learned distance measure and label confidence. The test result on the PSB data set has shown that the method making use of the unlabeled samples has achieved a better annotation result when a small amount of labels are given.

Key words: model annotation, metric learning, relevant component analysis