Probabilistic Feature Relevance Learning for Content-Based Image Retrieval

  title={Probabilistic Feature Relevance Learning for Content-Based Image Retrieval},
  author={Jing Peng and Bir Bhanu and Shan Qing},
  journal={Computer Vision and Image Understanding},
Most of the current image retrieval systems use “one-shot” queries to a database to retrieve similar images. Typically a K nearest neighbor kind of algorithm is used, where weights measuring feature importance along each input dimension remain fixed (or manually tweaked by the user), in the computation of a given similarity metric. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual… CONTINUE READING
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