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Automatic image annotation is crucial for keyword-based image retrieval. There is a trend focusing on utilization of machine learning techniques, which learn statistical models from annotated images and apply them to generate annotations for unseen images. In this paper we propose MAGMA - new image auto-annotation
the semantic keyword and the unlabeled image estimated on the obtained relevant topic is more accurate. The experimental results on the ECCV2002 benchmark (P. Duygulu et al., 2002) show that our method outperforms state-of-the-art annotation models MBRM and ASVM-MIL.
Automatic image annotation is a promising solution to enable more effective image retrieval by keywords. Traditionally, statistical models for image auto-annotation predicate each annotated keyword independently without considering the correlation of words. In this paper, we propose a novel probability model, in which
Automatic image annotation is a promising solution to enable more effective image retrieval by keywords. Different statistical models and machine learning methods have been introduced for image auto-annotation. In this paper, we propose a collaborative approach, in which multiple different statistical models are
, the improved model is capable of discovering the correlation between blobs (segmented regions) and textual keywords so as to automatically generate keywords for un-annotated image according to joint probabilities. Moreover, it has the ability to detect and remove false keyword(s) by considering the co-occurrence of
database is annotated with keywords. We present and evaluate a new method which improves the effectiveness of content-based image retrieval, by integrating semantic concepts extracted from text. Our model is inspired from the probabilistic graphical model theory: we propose a hierarchical mixture model which enables to handle
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