Shape Similarity System driven by Digital Elevation Models for Non-rigid Shape Retrieval (original) (raw)
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Non-rigid and partial 3D model retrieval are two significant and challenging research directions in the field of 3D model retrieval. Little work has been done in proposing a hybrid shape descriptor that works for both retrieval scenarios, let alone the integration of the component features of the hybrid shape descriptor in an automatic way. In this paper, we propose a hybrid shape descriptor that integrates both geodesic distance-based global features and curvature-based local features. We also develop an automatic algorithm to generate meta similarity resulting from different component features of the hybrid shape descriptor based on Particle Swarm Optimization. Experimental results demonstrate the effectiveness and advantages of our framework, as well as the significant improvements in retrieval performances. The framework is general and can be applied to similar approaches that integrate more features for the development of a single algorithm for both non-rigid and partial 3D model retrieval.
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More and more images have been generated in digital form around the world. There is a growing interest in finding images in large collections or from remote databases. In order to find an image, the image has to be described or represented by certain features. Shape is an important visual feature of an image. Searching for images using shape features has attracted much attention. There are many shape representation and description techniques in the literature. Object classification often operates by making decisions based on the values of several shape properties measured from an image of the object. Shape analysis is a useful tool for recognition of an object. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem of global contour and region based shape analysis, selecting the properties of the similarity measure that are needed for the problem and choosing the specific similarity measure to compute the similarity.
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