Authors:
Harald Grabner
1
;
Torsten Ullrich
1
and
Dieter W. Fellner
2
Affiliations:
1
Institute of Computer Graphics and Knowledge Visualization (CGV) and Technische Universität Graz, Austria
;
2
Technische Universität Darmstadt & Fraunhofer IGD, Institute of Computer Graphics and Knowledge Visualization (CGV) and Technische Universität Graz, Germany
Keyword(s):
Generative Modeling, Procedural Modeling, 3D-Retrieval, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Fundamental Methods and Algorithms
;
Geometric Computing
;
Geometry and Modeling
;
Modeling and Algorithms
;
Scene and Object Modeling
Abstract:
A digital library for non-textual, multimedia documents can be defined by its functionality: markup, indexing, and retrieval. For textual documents, the techniques and algorithms to perform these tasks are well studied. For non-textual documents, these tasks are open research questions: How to markup a position on a digitized statue? What is the index of a building? How to search and query for a CAD model? If no additional, textual information is available, current approaches cluster, sort and classify non-textual documents using machine learning techniques, which have a cold start problem: they either need a manually labeled, sufficiently large training set or the (automatic) clustering / classification result may not respect semantic similarity. We solve this problem using procedural modeling techniques, which can generate arbitrary training sets without the
need of any “real” data.
The retrieval process itself can be performed with any method. In this article we describe the histo
gram of inverted distances in detail and compare it to salient local visual features method. Both techniques are evaluated using the Princeton Shape Benchmark (Shilane et al., 2004). Furthermore, we improve the retrieval results by diffusion processes.
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