We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems in...
Derek Hoiem, Rahul Sukthankar, Henry Schneiderman,...
Increasing applications are demanding effective and efficient support to perform retrieval in large collections of digital images. The work presented here is an early stage resear...
Giovanna Castellano, Gianluca Sforza, Maria Alessa...
In many practical applications, ontologies tend to be very large and complicated. In order for users to quickly understand and analyze large-scale ontologies, in this paper we prop...
Kewei Tu, Miao Xiong, Lei Zhang, Haiping Zhu, Jie ...
We present a framework and an application for semanticbased retrieval of images. Our approach adopts a two-level ontology structure in a subset of OWL-DL. In the core ontology onl...
Tommaso Di Noia, Eugenio Di Sciascio, Francesco di...
Although much progress has been made, current lowlevel based visual information retrieval technology does not allow users to formulate queries through high-level semantics. More a...