Non-rigid 3D Model Retrieval Based on Weighted Bags-of-Phrases and LDA

This paper presents an improved BOP model, called weighted bags-of-phrases (W-BOP), and its application in non-rigid 3D shape retrieval. The W-BOP model uses the Gaussian weighted function and the 3D points’ ring based neighborhood to construct the spatial arrangement model of visual words. Compared with BOP model, it can describe the 3D model more detailedly. Compared with the SS-BOW model and the BOFG model, it needn’t perform feature detection step and has higher computation efficiency. To further improve the retrieval performance, the LDA algorithm is used to reduce the dimension of the W-BOP descriptor. Extensive experiments have validated the effectiveness of the designed W-BOP model and LDA based non-rigid 3D model retrieval method.