Similarity Learning Via Dissimilarity Space in CBIR G. P. Nguyen, M. Worring, A. W. M. Smeulders In ACM International Multimedia Conference and Exhibition 2006. [bibtex] [pdf] [url]

In this paper, we introduce a new approach to learn dissimilarity for interactive search in content based image retrieval. In literature, dissimilarity is often learned via the feature space by feature selection, feature weighting or a parameterized function of the features. Different from existing techniques, we use relevance feedback to adjust dissimilarity in a dissimilarity space. To create a dissimilarity space, we use Pekalska’s method [15]. After the user gives feedback, we apply active learning with one-class SVM on this space. Results on a Corel dataset of 10000 images and a TrecVid collection of 43907 key frames show that our proposed approach can improve the retrieval performance over the feature space based approach.

@InProceedings{NguyenIMCE2006,
  author       = "Nguyen, G. P. and Worring, M. and Smeulders, A. W. M.",
  title        = "Similarity Learning Via Dissimilarity Space in CBIR",
  booktitle    = "ACM International Multimedia Conference and Exhibition",
  pages        = "107--116",
  year         = "2006",
  url          = "https://ivi.fnwi.uva.nl/isis/publications/2006/NguyenIMCE2006",
  pdf          = "https://ivi.fnwi.uva.nl/isis/publications/2006/NguyenIMCE2006/NguyenIMCE2006.pdf",
  has_image    = 1
}