On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Shape-Similarity Comparison of 3D Models Using Alpha Shapes
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
SMI '04 Proceedings of the Shape Modeling International 2004
Learning semantic categories for 3D model retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Autotagging to improve text search for 3d models
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Ranking on semantic manifold for shape-based 3d model retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
Manifold-ranking based retrieval using k-regular nearest neighbor graph
Pattern Recognition
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This paper describes an automatic annotation, or autotagging, algorithm that attaches textual tags to 3D models based on their shape and semantic classes. The proposed method employs Manifold Ranking by Zhou et al, an algorithm that takes into account both local and global distributions of feature points, for tag relevance computation. Using Manifold Ranking, our method propagates multiple tags attached to a training subset of models in a database to the other tag-less models. After the relevance values for multiple tags are computed for tag-less points, the method selects, based on the distribution of feature points for each tag, the threshold at which the tag is selected or discarded for the points. Experimental evaluation of the method using a text-based 3D model retrieval setting showed that the proposed method is effective in autotagging 3D shape models.