Relevance feedback approach for image retrieval combining support vector machines and adapted Gaussian mixture models

@inproceedings{Marakakis2011RelevanceFA,
  title={Relevance feedback approach for image retrieval combining support vector machines and adapted Gaussian mixture models},
  author={Apostolos Marakakis and George Siolas and Nikolaos P. Galatsanos and Aristidis Likas and Andreas Stafylopatis},
  year={2011}
}
A new relevance feedback (RF) approach for content-based image retrieval (CBIR) is presented, which uses Gaussian mixture (GM) models as image representations. The GM of each image is obtained as an adaptation of a universal GM which models the probability distribution of the features of the image database. In each RF round, the positive and negative examples provided by the user until the current round are used to train a support vector machine (SVM) to distinguish between the relevant and… CONTINUE READING
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