@inproceedings{8b7f8b98c66645f1be9cf1323bb50051,
title = "Exploring discriminative views for 3D object retrieval",
abstract = "View-based 3D object retrieval techniques have become prevalent 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{\textquoteright}s views. Different from previous works on selecting representative views of query object, we investigate the discriminative information of each view in dataset. 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 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.",
keywords = "3D object Retrieval, Discriminative view, Reverse sum-min distance",
author = "Dong Wang and Bin Wang and Sicheng Zhao and Hongxun Yao and Hong Liu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 22nd International Conference on MultiMedia Modeling, MMM 2016 ; Conference date: 04-01-2016 Through 06-01-2016",
year = "2016",
doi = "10.1007/978-3-319-27671-7\_63",
language = "英语",
isbn = "9783319276700",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "755--766",
editor = "Qi Tian and Richang Hong and Xueliang Liu and Nicu Sebe and Benoit Huet and Guo-Jun Qi",
booktitle = "MultiMedia Modeling - 22nd International Conference, MMM 2016, Proceedings",
address = "德国",
}