Classification and Automatic Annotation Extension of Images Using Bayesian Network
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Modeling, classifying and annotating weakly annotated images using Bayesian network
Journal of Visual Communication and Image Representation
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We present a new framework which tries to improve the effectiveness of CBIR by integrating semantic concepts extracted from text. Our model is inspired from the VSM model developed in information retrieval. We represent each image in our collection with a vector of probabilities linking it to the different keywords. In addition to the semantic content of images, these probabilities capture the user's preference in each step of relevance feedback. The obtained features are then combined with visual ones in retrieval phase. Evaluation carried out on more than 10,000 images shows that this considerably improves retrieval effectiveness.