Sketch-Based 3D Model Retrieval Utilizing Adaptive View Clustering and Semantic Information
Document Type
Article
Publication Date
12-1-2017
School
Computing Sciences and Computer Engineering
Abstract
© 2016, Springer Science+Business Media New York. 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, human computer interaction, 3D animation, game design, and etc. In this paper, our target is to significantly improve the current sketch-based 3D retrieval performance in terms of both accuracy and efficiency. We propose a new sketch-based 3D model retrieval framework by utilizing adaptive view clustering and semantic information. It first utilizes a proposed viewpoint entropy-based 3D information complexity measurement to guide adaptive view clustering of a 3D model to shortlist a set of representative sample views for 2D-3D comparison. To bridge the gap between the query sketches and the target models, we then incorporate a novel semantic sketch-based search approach to further improve the retrieval performance. Experimental results on several latest benchmarks have evidently demonstrated our significant improvement in retrieval performance.
Publication Title
Multimedia Tools and Applications
Volume
76
Issue
24
First Page
26603
Last Page
26631
Recommended Citation
Li, B.,
Lu, Y.,
Johan, H.,
Fares, R.
(2017). Sketch-Based 3D Model Retrieval Utilizing Adaptive View Clustering and Semantic Information. Multimedia Tools and Applications, 76(24), 26603-26631.
Available at: https://aquila.usm.edu/fac_pubs/18430
Comments
This is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s11042-016-4187-3.