View-based 3D object retrieval with discriminative views

View-based 3D object retrieval techniques have become increasingly important in various fields, and lots of ingenious studies have promoted the development of retrieval performance from different aspects. In this paper, we focus on the 2D projective views that represent the 3D objects and propose a boosting approach by evaluating the discriminative ability of each object's views. The dissimilarity between views' semantic and discriminative ability is firstly investigated through classification performance. We then propose a simple, robust and effective measurement to study the view's discriminative ability. By employing the proposed reverse distance metric, we utilize the discriminative information for many to many view set matching. The proposed algorithm is then employed with various features to boost the multi-model graph learning based retrieval method. We compare our approach with several state of the art methods on ETH-80 dataset and National Taiwan University 3D model dataset. The results demonstrate the effectiveness of our method and its excellent boosting performance.