Unsupervised Feature Learning with Graph Embedding for View-Based 3D Model Retrieval

Abstract

3D model retrieval is becoming a hot research topic due to its wide applications such as computer-aided design, digital entertainment, and virtual reality. For this challenging task, feature learning and similarity measure are two critical problems. However, existing approaches usually learn discriminative visual features and develop a complex graph matching strategy to measure the similarity independently. In this paper, we propose an unsupervised method which can embed similarity measure into the feature space. The proposed method utilizes both similarity and dissimilarity information to better leverage the unsupervised problem and estimates the labels which are further used for metric learning. With the learned metric, we project the original features to more discriminative feature space and efficiently measure the similarity among models under the new feature space. We conduct extensive evaluations of three popular and challenging datasets. The experimental results demonstrate the superiority and effectiveness of the proposed method, competing against the state of the arts.