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
Effective automatic image annotation via a coherent language model and active learning
Proceedings of the 12th annual ACM international conference on Multimedia
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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An approach to automatic image annotation is proposed. Generally, the relation between visual characteristics and the annotation label is estimated from the annotated corpus and is used to predict label for new test image. Unfortunately, when limited number of images are annotated, with possible multiple labels per image, this relation cannot be reliably estimated. To cope with this problem, we propose taking into account information derived directly from other images in the dataset. This method extends naturally to semi-supervised setting where un-annotated images are also used select annotation labels. Experiment shows that the proposed method yields promising results.