One-class SVM for learning in image retrieval

Relevance feedback schemes using linear/quadratic estimators have been applied in content-based image retrieval to improve retrieval performance significantly. One major difficulty in relevance feedback is to estimate the support of target images in high dimensional feature space with a relatively small number of training samples. We develop a novel scheme based on one-class SVM, which fits a tight hyper-sphere in the nonlinearly transformed feature space to include most of the target images based on positive examples. The use of a kernel provides us an elegant way to deal with nonlinearity in the distribution of the target images, while the regularization term in SVM provides good generalization ability. To…

• NS
  National Science Foundation