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
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
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective automatic image annotation via a coherent language model and active learning
Proceedings of the 12th annual ACM international conference on Multimedia
The Definitive Guide to db4o
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Automated annotation of digital images is a challenging task being used for indexing, retrieving, and understanding of large collections of image data. Several machine-learning approached have been proposed to model the existing associations between words and images. Each approach is trying to assign to a test image some meaningful words taking into account a set of feature vectors extracted from that image. This paper presents an original image annotation system based on an open source object database called db4o. An object oriented model offers suport for storing complex objects as sets, lists, trees or other advanced data structures. The information needed for the annotation process is retrieved from the SAIAPR TC-12 Dataset - a set of annotated images having a vocabulary with a hierarchical structure. The annotation system is using an efficient annotation model called Cross Media Relevance Model.