3D object retrieval based on Spatial+LDA model

Weizhi Nie, Xixi Li, Anan Liu, Yuting Su

Abstract: Latent Dirichlet Allocation (LDA) is one popular topic extraction method, which has been applied in many applications such as textual retrieval, user recommendation system and video cluster. In this paper, we apply LDA model for visual topics extraction and utilized the topic distribution visual feature of image to handle 3D object retrieval problem. Different from the traditional LDA model, we add the spatial information of visual feature for document generation. First, we extract SIFT features from each 2D image extracted from 3D object. Then, we structure the visual documents according to the spatial information of 3D model. Finally, LDA model is used to extract the topic model for handling the retrieval problem. We further propose a multi-topic model to improve retrieval performance. Extensive comparison experiments were on the popular ETH, NTU and MV-RED 3D model datasets. The results demonstrate the superiority of the proposed method.

External IDs:dblp:journals/mta/NieLLS17