Article ID Journal Published Year Pages File Type
412042 Neurocomputing 2015 8 Pages PDF
Abstract

In view-based 3D object retrieval, each object is represented by a set of image views. 3D object retrieval becomes a group matching problem under such definition. Recent works have shown the effectiveness of hypergraph learning that computes the distance between 3D objects by solving a hypergraph structure problem. However, the single feature used in most of state-of-the-art works is often not sufficient to describe a 3D object. In this paper, we propose a feature fusion method based on hypergraph for 3D object retrieval. Besides the frequently used Zernike moments feature, we propose a Dense Kernel Local Binary Feature (DKLBP) feature for 3D object view description. A feature fusion method is proposed under the hypgraph framework. Experiments are conducted on the popular ETH-80 and National Taiwan University 3D model datatsets. Extensive experimental results show that the proposed approach has made significant performance improvement compared to other competitive approaches in recent works.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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