
Computer Vision/Computer Graphics Collaboration Techniques
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Automatic Combination of Feature Descriptors for E.ective 3D Shape Retrieval (p.49)
Abstract.
We focus on improving the effectiveness of content-based 3D shape retrieval. Motivated by retrieval performance of several individual 3D model feature vectors, we propose a novel method, called prior knowledge based automatic weighted combination, to improve the retrieval effectiveness. The method dynamically determines the weighting scheme for different feature vectors based on the prior knowledge. The experimental results show that the proposed method provides significant improvements on retrieval e.ectiveness of 3D shape search with several measures on a standard 3D database. Compared with two existing combination methods, the prior knowledge weighted combination technique has gained better retrieval effectiveness.
1 Introduction
With the rapid development of 3D scanner technology, graphic hardware, and the World-Wide Web, there has been an explosion in the number of 3D models available on the Internet. In order to make use of these 3D models, the techniques of effective 3D shape retrieval become increasingly significant. 3D models can be annotated by keywords at first, facilitating the text-based retrieval. However, this is not a promising approach, because generally annotations are manually created, which is prohibitively expensive and subject to some factors.
To overcome the disadvantages of annotation-based approach, the so-called contentbased 3D shape retrieval, using the 3D model itself, has been proposed as an alternative mechanism [9]. In [17], Min compared four text annotation-based matching methods and four content-based retrieval approaches, and the experiments showed that the relatively simple solution of using only associated text for retrieval of 3D model was not as effective as using their shape.
As a promising approach applied in many fields, the content-based 3D shape retrieval has attracted many researchers in recent years. In the computer aided design [23], the similar search for standard parts is handy in helping to reach at higher speed with lower cost. In bioinformatics [11], the detection and retrieval of similar protein molecules is applied. Other cases of using this method can be found in the entertainment industry, visual reality, and so forth.
In this paper, we experimentally compare a range of di.erent 3D feature vectors, and the experimental results show that the relative ordering of feature vectors by retrieval effectiveness depends on query models or model classes, which means that no single feature vector can always outperform other feature vectors on all query models. To address the issue and improve the effectiveness of content-based 3D shape retrieval, we propose a novel method, called prior knowledge based automatic weighted combination, which provides significant improvements on retrieval effectiveness of content-based 3D shape search.
Compared with two existing methods, one is using entropy impurity, the other is based on purity-weighted, our method achieves better 3D shape retrieval performance. The rest of this paper is organized as follows. Section 2 introduces the similarity search of 3D objects about feature-based approaches and some feature vectors. Effectiveness measures and retrieval performance for single feature vectors are described in Section 3.
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