SHREC'08 entry: Semi-supervised learning for semantic 3D model retrieval

A shape feature by itself is not sufficient for effective 3D model retrieval. Long-lasting semantics shared by a community as well as a short-lived intention of a user determines the similarity of 3D models. In this paper, we describe a method of shape-based 3D model retrieval that employs off-line, semi-supervised learning of multiple classes in the database to capture long-lasting, shared semantic knowledge. The method performs two learning based dimension reductions, first one to accommodate distribution of features in the feature space and the second one to accommodate the semantic knowledge embodied in a set of user-defined semantic labels. We evaluate the method by using the SHREC’08 3D Generic and CAD Models Track.

• Type: Conference
• Year: 2008
• Where: SMI
• Authors: Akihiro Yamamoto, Masaki Tezuka, Toshiya Shimizu, Ryutarou Ohbuchi
• Keywords: 3D Model, Image Analysis, Model Retrieval, Semantic Knowledge, SMI 2008