In many image retrieval applications, the mapping between highlevel semantic concept and low-level features is obtained through a learning process. Traditional approaches often as...
We address the challenge of semantic gap reduction for image retrieval through an improved SVM-based active relevance feedback framework, together with a hybrid visual and concept...
Aggregating search results from a variety of heterogeneous sources or verticals such as news, image and video into a single interface is a popular paradigm in web search. Although...
Ke Zhou, Ronan Cummins, Mounia Lalmas, Joemon M. J...
Motivated by the need to efficiently leverage user relevance feedback in content-based retrieval from image databases, we propose a fast, clustering-based indexing technique for e...
In this project (VIRSI) we investigate the promising contentbased retrieval paradigm known as interactive search or relevance feedback, and aim to extend it through the use of syn...
Bart Thomee, Mark J. Huiskes, Erwin M. Bakker, Mic...