In this paper we introduce a new approach aimed at solving the problem of image retrieval from text queries. We propose to estimate the word relevance of an image using a neighborhood-based estimator. This estimation is obtained by counting the number of word-relevant images among the K-neighborhood of the image. To this end a Bayesian approach is adopted to define such a neighborhood. The local estimations of all the words that form a query are naively combined in order to score the images according to that query. The experiments show that the results are better and faster than the state-of-the-art techniques. A special consideration is done for the computational behaviour and scalability of the proposed approach.


Image Retrieval Visual Word Training Image Image Representation Image Annotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Roberto Paredes
    • 1
  1. 1.ITI-UPVValencia(Spain)

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