Computational geometry: an introduction
Computational geometry: an introduction
Multimedia Systems - Special issue on content-based retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Studies in computational geometry motivated by mesh generation
Studies in computational geometry motivated by mesh generation
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
An effective method for locally neighborhood graphs updating
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Neighborhood graphs for indexing and retrieving multi-dimensional data
Journal of Intelligent Information Systems
Using semantic distance in a content-based heterogeneous information retrieval system
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
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Images annotation is the main tool for associating a semantic to an image. In this article we are interested in the semi-automatic annotation of images data. Indeed, with the great mass of data managed throughout the world and especially with the Web, the manual annotation of these images is almost impossible. We propose an approach based on neighborhood graphs offering several possibilities: content-based retrieval, key-words based interrogation, and the annotation which concerns us in this article. The approach we are proposing offers, as the experiments section shows it, very interesting annotation results while satisfying the scalability criteria which is a very significant point in this context where the mass of data is very important.