Unsupervised 3D object retrieval with parameter-free hierarchical clustering

In 3D object retrieval, additional knowledge like user input, classification information or database dependent configured parameters are rarely available in real scenarios. For example, meta data about 3D objects is seldom if the objects are not within a well-known evaluation database. We propose an algorithm which improves the performance of unsupervised 3D object retrieval without using any additional knowledge. For the computation of the distances in our system any descriptor can be chosen; we use the Panorama-descriptor. Our algorithm uses a precomputed parameter-free agglomerative hierarchical clustering and combines the information of the hierarchy of clusters with the individual distances to improve a single object query. Additionally, we propose an adaption algorithm for the cases that new objects are added frequently to the database. We evaluate our approach with 6 databases including a total of 13271 objects in 481 classes. We show that our algorithm improves the average precision in an unsupervised scenario without any parameter configuration.