Name
Abstract | ||
|---|---|---|
This paper presents a novel 3D shape retrieval method, which uses Bag-of-Features and an efficient multi-view shape matching scheme. In our approach, a properly normalized object is first described by a set of depth-buffer views captured on the surrounding vertices of a given unit geodesic sphere. We then represent each view as a word histogram generated by the vector quantization of the view's salient local features. The dissimilarity between two 3D models is measured by the minimum distance of their all (24) possible matching pairs. This paper also investigates several critical issues including the influence of the number of views, codebook, training data, and distance function. Experiments on four commonly-used benchmarks demonstrate that: 1) Our approach obtains superior performance in searching for rigid models. 2) The local feature and global feature based methods are somehow complementary. Moreover, a linear combination of them significantly outperforms the state-of-the-art in terms of retrieval accuracy. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/SMI.2010.20 | Shape Modeling International |
Keywords | Field | DocType |
image coding,commonly-used benchmarks,depth-buffer views,image matching,retrieval accuracy,local descriptor,vector quantization,distance function,visual similarity,possible matching pair,3d shape retrieval,bag-of-features,codebook,minimum distance,vector quantisation,unit geodesic sphere,global feature,efficient multi-view shape,image retrieval,depth-buffer view,shape retrieval,visual similarity based 3d shape retrieval method,word histogram,local feature,multiview shape matching scheme,shape retrieval method,sift,computer vision,solid modeling,histograms,construction industry,visualization,automation,shape,training data,active shape model,nist,feature extraction | Scale-invariant feature transform,Computer vision,Active shape model,Histogram,Pattern recognition,Computer science,Image retrieval,Metric (mathematics),Feature extraction,Vector quantization,Artificial intelligence,Codebook | Conference |
ISBN | Citations | PageRank |
978-1-4244-7260-4 | 55 | 1.65 |
References | Authors | |
29 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou-hui Lian | 1 | 475 | 32.27 |
| Afzal Godil | 2 | 619 | 30.70 |
| Xian-Fang Sun | 3 | 614 | 34.84 |