关键词: 三维模型分类, 多标签, 标签传播

Abstract: With the progress of data acquisition equipment and modeling technology, how to efficiently analyze and retrieve 3D shapes has become a hot topic in the field of geometric processing. Currently, many works are focused on the classification of models, almost only consider single label. When dealing with the multi-label problem, the correlation between tags and samples is ignored ,as a result, a lot of computation time is spent. To address this problem, a 3d model labeling method based on multi-label propagation is proposed. The core of this approach is to explore the potential of multi-label correlation in the whole sample space by using the label and sample correlation. In particular, a small set of multi-labeled samples are given, it iterates dynamic metric fusion with label information and the label information of these samples are propagated to the unlabeled samples. The process of propagation depends on similarity matrices between samples and the relationship between labels and samples is taken into account. The labeling results are gotten until the iteration finish. Experimental results on the PSB dataset demonstrates the high accuracy of the approach with a few interactions.

Key words: 3D shape classification, multi-label, label propagation