In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques i...
Haiming Liu 0002, Victoria S. Uren, Dawei Song, St...
We introduce GaZIR, a gaze-based interface for browsing and searching for images. The system computes on-line predictions of relevance of images based on implicit feedback, and wh...
An effective graphic interface is a key tool to improve the fruition of the results retrieved by an Information Retrieval (IR) system. In this work, we describe a two-dimensional...
Lorenzo De Stefani, Giorgio Maria Di Nunzio, Giorg...
Relevance Feedback has proven very effective for improving retrieval accuracy. A difficult yet important problem in all relevance feedback methods is how to optimally balance the...
Relevance Feedback is an important way to enhance retrieval quality by integrating relevance information provided by a user. In XML retrieval, feedback engines usually generate an ...
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections...
Our goal is to determine if artificially imagined or synthesized images can be beneficial to interactive visual search. We present a novel approach for using artificially imagined...
Bart Thomee, Mark J. Huiskes, Erwin M. Bakker, Mic...
Users of image databases often prefer to retrieve relevant images by categories. Unfortunately, images are usually indexed by low-level features like color, texture and shape, whi...
When using relevance feedback for the interactive categorization of images, the strategy employed by the system to select images to be presented to the user is of paramount import...
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...