Camera Constraint-Free View-Based 3-D Object Retrieval

Recently, extensive research efforts have been dedicated to view-based methods for 3-D object retrieval due to the highly discriminative property of multiviews for 3-D object representation. However, most of state-of-the-art approaches highly depend on their own camera array settings for capturing v...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 21; no. 4; pp. 2269 - 2281
Main Authors: Yue Gao, Jinhui Tang, Hong, Richang, Shuicheng Yan, Qionghai Dai, Naiyao Zhang, Tat-Seng Chua
Format: Journal Article
Language:English
Published: United States IEEE 01.04.2012
Subjects:
3-D object
Algorithms
Arrays
Camera constraint-free
Cameras
Computational modeling
Educational institutions
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Pattern Recognition, Automated - methods
Photography - methods
Reproducibility of Results
retrieval
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Solid modeling
Subtraction Technique
Three dimensional displays
view-based
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Recently, extensive research efforts have been dedicated to view-based methods for 3-D object retrieval due to the highly discriminative property of multiviews for 3-D object representation. However, most of state-of-the-art approaches highly depend on their own camera array settings for capturing views of 3-D objects. In order to move toward a general framework for 3-D object retrieval without the limitation of camera array restriction, a camera constraint-free view-based (CCFV) 3-D object retrieval algorithm is proposed in this paper. In this framework, each object is represented by a free set of views, which means that these views can be captured from any direction without camera constraint. For each query object, we first cluster all query views to generate the view clusters, which are then used to build the query models. For a more accurate 3-D object comparison, a positive matching model and a negative matching model are individually trained using positive and negative matched samples, respectively. The CCFV model is generated on the basis of the query Gaussian models by combining the positive matching model and the negative matching model. The CCFV removes the constraint of static camera array settings for view capturing and can be applied to any view-based 3-D object database. We conduct experiments on the National Taiwan University 3-D model database and the ETH 3-D object database. Experimental results show that the proposed scheme can achieve better performance than state-of-the-art methods.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2011.2170081