Refine Search

New Search

Results: 5

(searched for: doi:10.13176/11.131)
Save to Scifeed
Page of 1
Articles per Page
by
Show export options
  Select all
Yongsheng Zhang, Tsuyoshi Yamamoto, Yoshinori Dobashi
Published: 1 September 2016
Neurocomputing, Volume 207, pp 684-692; https://doi.org/10.1016/j.neucom.2016.05.053

, Xuan Liu, Qiang Cai,
Published: 12 September 2015
Lecture Notes in Computer Science, Volume 8971, pp 3-18; https://doi.org/10.1007/978-3-662-48247-6_1

The publisher has not yet granted permission to display this abstract.
IEEE Transactions on Image Processing, Volume 24, pp 1449-1459; https://doi.org/10.1109/tip.2015.2395961

Abstract:
3D object retrieval has attracted extensive research efforts and become an important task in recent years. It is noted that how to measure the relevance between 3D objects is still a difficult issue. Most of the existing methods employ just the model-based or view-based approaches, which may lead to incomplete information for 3D object representation. In this paper, we propose to jointly learn the view-model relevance among 3D objects for retrieval, in which the 3D objects are formulated in different graph structures. With the view information, the multiple views of 3D objects are employed to formulate the 3D object relationship in an object hypergraph structure. With the model data, the model-based features are extracted to construct an object graph to describe the relationship among the 3D objects. The learning on the two graphs is conducted to estimate the relevance among the 3D objects, in which the view/model graph weights can be also optimized in the learning process. This is the first work to jointly explore the view-based and model-based relevance among the 3D objects in a graph-based framework. The proposed method has been evaluated in three data sets. The experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness on retrieval accuracy of the proposed 3D object retrieval method.
Ke Lu, Rongrong Ji, Jinhui Tang, Yue Gao
IEEE Transactions on Image Processing, Volume 23, pp 4553-4563; https://doi.org/10.1109/tip.2014.2343460

Abstract:
Distance measure between two sets of views is one central task in view-based 3D model retrieval. In this paper, we introduce a distance metric learning method for bipartite graph matching-based 3D object retrieval framework. In this method, the relationship among 3D models is formulated by a graph structure with semisupervised learning to estimate the model relevance. More specially, we model two sets of views by using a bipartite graph, on which their optimal matching is estimated. Then, we learn a refined distance metric by using the user's relevance feedback. The proposed method has been evaluated on four data sets and the experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed method.
, , Hongtao Xie
Advances in Intelligent and Soft Computing, Volume 128, pp 1-5; https://doi.org/10.1007/978-3-642-25989-0_1

The publisher has not yet granted permission to display this abstract.
Page of 1
Articles per Page
by
Show export options
  Select all
Back to Top Top