By using relevance feedback [6], Content-Based Image Retrieval (CBIR) allows the user to retrieve images interactively. The user can select the most relevant images and provide a ...
Much of the world’s data is in the form of time series, and many other types of data, such as video, image, and handwriting, can easily be transformed into time series. This fact...
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Closing the semantic gap in content based image retrieval (CBIR) basically requires the knowledge of the user's intention which is usually translated into a sequence of quest...
Relevance feedback is an attractive approach to developing flexible metrics for content-based retrieval in image and video databases. Large image databases require an index struct...