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Shape-Similarity Search of Three-Dimensional Models Based on Subjective Measures

As popularity of three-dimensional (3D) models increase, interests in shape-similarity search of 3D models have increased. Except for a few, most of the methods for 3D shape similarity search published so far does not consider human subjective measures. In this paper, we propose a new shape-similarity search method that combines a 3D shape feature that is independent of the model pose and size with a learning classifier called Support Vector Machine (SVM). The system is a human-directed query-by-example system. By tagging similar and dissimilar models among the list of current retrieval results, the system learns the model the user desires by using the SVM. Preliminary experimental results shows that, despite its simplicity, the system works well in retrieving shape a user feels “similar” to the examples given.