Hierarchical indexing for 3D head model retrieval based on Kernel PCA

In this paper, a novel 3D head model retrieval framework is proposed. First, Kernel PCA is adopted both to reduce the data dimension and to extract features for model characterization. Second, based on the derived features, a hierarchical indexing structure for 3D model database is constructed using the Hierarchical Self Organizing Map (HSOM). Third, an efficient search approach is presented based on the established indexing structure that requires only feature matching between the query model and a small number of SOM nodes. The main advantages of our approach include high retrieval precision due to the discrimination capacity of kernel PCA, and low computation cost due to the hierarchical indexing structure and data dimension reduction. In addition, the topology-preserving property of HSOM also facilitates the exploration of the model database with the possibility of further knowledge discovery.