Abstract. This paper explores how to predict query difficulty for contextual image retrieval. We reformulate the problem as the task of predicting how difficult to represent a quer...
We present a model for image retrieval in which images are represented both at the form level, as sets of physical features of the representing objects, and at the content level, a...
Carlo Meghini, Fabrizio Sebastiani, Umberto Stracc...
Abstract. We present a model for complex documents possibly consisting of a hierarchically structured set of images or texts. Documents are represented both at the form level (as s...
Carlo Meghini, Fabrizio Sebastiani, Umberto Stracc...
Today's Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (kNN) model. They retrieve images from a single neighborhood using lo...
Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which ha...
Ondrej Chum, James Philbin, Josef Sivic, Michael I...