Three-dimensional alpha shapes
ACM Transactions on Graphics (TOG)
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Content based retrieval of VRML objects: an iterative and interactive approach
Proceedings of the sixth Eurographics workshop on Multimedia 2001
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Learning semantic categories for 3D model retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Shape-Based Autotagging of 3D Models for Retrieval
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
3D model retrieval using weighted bipartite graph matching
Image Communication
A unified framework for recommending diverse and relevant queries
Proceedings of the 20th international conference on World wide web
Manifold-ranking based retrieval using k-regular nearest neighbor graph
Pattern Recognition
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Semantics associated with 3D shapes are often as important as the shapes themselves in defining "shape similarity" among them. So far, only a small subset of 3D model retrieval methods took semantics into account. Most popular approach to semantic 3D model retrieval is based on Relevance Feedback (RF), an iterative, interactive approach for a system to learn a semantic class that embodies "user intention" for the query. A drawback of a typical RF-based method is its low initial performance as it starts cold without any semantic knowledge. An alternative approach is off-line learning of multiple semantic classes. The approach produces a good retrieval performance without per-query training iterations, but is unable to capture user intention per-query. The method proposed in this paper attempts to combine benefits of the two approaches so that both shared multiple semantic classes and per-query intention can be captured to improve 3D model retrieval. Our method first learns, off-line, the multiple semantic classes by using a semi-supervised manifold learning algorithm to produce a "semantic manifold" of the input features. The RF iteration based on manifold ranking algorithm is then run on the semantic manifold. Our empirical evaluation showed that this method significantly outperforms the manifold ranking run in the original, ambient feature space.