Paper Info
Title
Sketch-based 3D model retrieval by viewpoint entropy-based adaptive view clustering
Abstract
Searching for relevant 3D models based on hand-drawn sketches is both intuitive and important for many applications, such as sketch-based 3D modeling and recognition. We propose a sketch-based 3D model retrieval algorithm by utilizing viewpoint entropy-based adaptive view clustering and shape context matching. Different models have different visual complexities, thus there is no need to keep the same number of representative views for each model. Motivated by this, we propose to measure the visual complexity of a 3D model by utilizing viewpoint entropy distribution of a set of sample views and based on the complexity value, we can adaptively decide the number of representative views. Finally, we perform Fuzzy C-Means based view clustering on the sample views based on their viewpoint entropy values. We test our algorithm on two latest sketch-based 3D model retrieval benchmarks and compare it with other four state-of-the-art approaches. The results demonstrate the superior performance and advantages of our algorithm.
Year
DOI
Venue
2013
10.2312/3DOR/3DOR13/049-056
3DOR
Keywords
Field
DocType
different model,different visual complexity,viewpoint entropy value,utilizing viewpoint entropy distribution,complexity value,adaptive view clustering,utilizing viewpoint,model retrieval algorithm,model retrieval benchmarks,representative view
Visual complexity,Data mining,Computer science,Fuzzy logic,Artificial intelligence,Cluster analysis,3D modeling,Retrieval algorithm,Shape context,Machine learning,Sketch
Conference
Citations 
PageRank 
References 
15
0.56
18
Authors
3
Name
Order
Citations
PageRank
Bo Li125715.09
Yijuan Lu273246.24
Henry Johan335529.36