An adaptive feature combination method based on ranking order for 3D model retrieval

Abstract

Directly combining several complementary features may increase the retrieval precision for 3D models. However, in most cases, we need to set the weights manually and empirically. In this paper, we propose a new schema for automatically choosing the proper weights for different features on each database. The proposed schema utilises the ranking order of the retrieval results, and it is invariant to the magnitude scaling. We choose the best feature as the standard one, and the relevance values between the standard and other features are the weights for feature combination. Furthermore, we propose an improved re-ranking algorithm for further improving the retrieval performance. Experiment shows the proposed method can automatically choose the proper weights for different features, and the experiment results on the existing features exceed the benchmark.