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

Lazaridis, Michalis; Daras, Petros

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

Files

049-056.pdf (300.56 KB)

Date

2008

Authors

Lazaridis, Michalis
Daras, Petros

Publisher

The Eurographics Association

Abstract

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).

        @inproceedings{:10.2312/3DOR/3DOR08/049-056
,
booktitle = {Eurographics 2008 Workshop on 3D Object Retrieval
},
editor = {Stavros Perantonis and Nikolaos Sapidis and Michela Spagnuolo and Daniel Thalmann
},
title = {{A Neurofuzzy Approach to Active Learning based Annotation Propagation for 3D Object Databases
}},
author = {Lazaridis, Michalis and 
Daras, Petros
},
year = {2008
},
publisher = {The Eurographics Association
},
ISSN = {1997-0463
},
ISBN = {978-3-905674-05-7
},
DOI = {/10.2312/3DOR/3DOR08/049-056
}
}