A Neurofuzzy Approach to Active Learning based Annotation Propagation for 3D Object Databases (English)

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Most existing Content-based Information Retrieval (CBIR) systems using semantic annotation, either annotate all the objects in a database (full annotation) or a manually selected subset (partial annotation) in order to increase the system's performance. As databases become larger, the manual effort needed for full annotation becomes unaffordable. In this paper, a fully automatic framework for partial annotation and annotation propagation, applied to 3D content, is presented. A part of the available 3D objects is automatically selected for manually annotation, based on their 'information content'. For the non-annotated objects, the annotation is automatically propagated using a neurofuzzy model, which is trained during the manual annotation process and takes into account the information hidden into the already annotated objects. Experimental results show that the proposed method is effective, fast and robust to outliers. The framework can be seen as another step towards bridging the semantic gap between low-level geometric characteristics (content) and intuitive semantics (context).

  • Title:
    A Neurofuzzy Approach to Active Learning based Annotation Propagation for 3D Object Databases
  • Author / Creator:
  • Published in:
  • Publisher:
    The Eurographics Association
  • Place of publication:
    Postfach 8043, 38621 Goslar, Germany
  • Year of publication:
    2008
  • Size:
    8 pages
  • ISBN:
  • ISSN:
  • DOI:
  • Type of media:
    Conference paper
  • Type of material:
    Electronic Resource
  • Language:
    English
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Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

1
Characterizing Shape Using Conformal Factors
Ben-Chen, Mirela / Gotsman, Craig | 2008
9
3D Object Retrieval using an Efficient and Compact Hybrid Shape Descriptor
Papadakis, Panagiotis / Pratikakis, Ioannis / Theoharis, Theoharis / Passalis, Georgios / Perantonis, Stavros | 2008
17
Isometry-invariant Matching of Point Set Surfaces
Ruggeri, Mauro R. / Saupe, Dietmar | 2008
25
Markov Random Fields for Improving 3D Mesh Analysis and Segmentation
Lavoué, Guillaume / Wolf, Christian | 2008
33
Part Analogies in Sets of Objects
Shalom, Shy / Shapira, Lior / Shamir, Ariel / Cohen-Or, Daniel | 2008
41
Similarity Score Fusion by Ranking Risk Minimization for 3D Object Retrieval
Akgül, Ceyhun Burak / Sankur, Bülent / Yemez, Yücel / Schmitt, Francis | 2008
49
A Neurofuzzy Approach to Active Learning based Annotation Propagation for 3D Object Databases
Lazaridis, Michalis / Daras, Petros | 2008
57
Face Recognition by SVMs Classification and Manifold Learning of 2D and 3D Radial Geodesic Distances
Berretti, Stefano / Bimbo, Alberto Del / Pala, Pietro / Mata, Francisco Josè Silva | 2008
65
A 3D Face Recognition Algorithm Using Histogram-based Features
Zhou, Xuebing / Seibert, Helmut / Busch, Christoph / Funk, Wolfgang | 2008
73
On-line and Open Platform for 3D Object Retrieval
Bonhomme, Benoit Le / Mustafa, B. / Celakovsky, Sasko / Preda, Marius / Preteux, Francoise / Davcev, D. | 2008
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